US9449275B2 - Actuation of a technical system based on solutions of relaxed abduction - Google Patents
Actuation of a technical system based on solutions of relaxed abduction Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/004—Error avoidance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Definitions
- the invention relates to a method and an apparatus for actuating a technical system.
- Model-based information interpretation (and the application thereof within the framework of model-based diagnosis) is becoming increasingly important.
- model-based methods have the advantage of an explicit and comprehensible description of the domain (e.g. of the technical system requiring a diagnosis).
- Such an explicit model can be examined and understood, which promotes acceptance by the user, particularly in respect of a diagnosis or an interpretation result.
- the models can be customized for new machines, extended by new domain knowledge and, depending on the type of presentation, even checked for correctness with reasonable effort. It is also possible to use a vocabulary of the model for man-machine interaction and hence for implementing an interactive interpretation process.
- the object is thus to determine, for a given model T (also called the “theory”) and a set of observations O, a set A of assumptions (usually as a subset A ⁇ A from all possible assumptions A) such that the observations O are explained by the model T and also the assumptions A ⁇ A.
- T also called the “theory”
- a ⁇ A from all possible assumptions A
- the problem is worded as an optimization problem, i.e. the “best” such set A ⁇ A of assumptions is sought (according to the optimality criterion, e.g. the smallest set, or the set with the lowest weight).
- the object of the invention is to avoid the disadvantages cited above and to allow an opportunity for abduction even in the case of erroneous observations.
- the object is achieved by proposing a method for actuating a technical system
- the actuation may relate to or comprise control, diagnosis or other processing of data from the technical system.
- the actuation in this case also comprises diagnosis, for example pertaining to the use of the information during a maintenance interval.
- the presented approach is highly generic, i.e. it does not require any assumptions about the preference relations used besides the intuitive stipulation that the addition of a further assumption (in the case of an unaltered observation set) cannot improve the preference and the addition of an explained observation (in the case of an unaltered assumption set) cannot impair the preference.
- the general definition—based on general orders—of the optimality allows the use of various optimality terms, for example minimum and/or maximum number of elements, subset and/or superset relationship, or minimum and/or maximum sum of the weights of the elements contained.
- the order ⁇ is based on the orders ⁇ A and ⁇ O as follows: ( A,O ) ⁇ ( A′,O ′) A ⁇ A A′ O ⁇ O O′ ( A,O ) ⁇ ( A′,O ′) ( A ⁇ A A′ O ⁇ O O ′) ( A ⁇ A A′ O ⁇ O O ′) ( A,O ) ⁇ ( A′,O ′) (( A,O ) ⁇ ( A′,O ′)) (( A,O ) ⁇ ( A′,O ′)) (( A,O ) ⁇ ( A′,O ′)) (( A,O ) ⁇ ( A′,O ′))
- the order ⁇ A is chosen to be monotone and the order ⁇ O is chosen to be anti-monotone for subset relationships.
- the relaxed abduction problem is solved by transforming the relaxed abduction problem into a hypergraph, so that tuples (A,O) are encoded by pareto-optimal paths in the hypergraph.
- hyperedges of the hypergraph are induced by transcriptions of prescribed rules.
- A,B ⁇ N C T ,r ⁇ N R ⁇ , wherein V T ⁇ ( A A ),( A T )
- a ⁇ N C T ⁇ ⁇ V denotes a set of final states and E denotes a set of the hyperedges e ( T ( e ), h ( e ), w ( e )), so that the following holds: there is an axiom a ⁇ T ⁇ A
- a subsequent embodiment is that shortest hyperpaths are determined by taking account of two preferences.
- the shortest hyperpaths are determined by taking account of two preferences by means of a label correction algorithm.
- An additional embodiment is that alterations along the hyperedges are propagated by means of a meet operator and/or by means of a join operator.
- Another embodiment is that the relaxed abduction problem is determined by means of a piece of description logic.
- the processing unit may, in particular, be a processor unit and/or an at least partially hardwired or logic circuit arrangement that, by way of example, is set up such that the method as described herein can be carried out.
- Said processing unit may be or comprise any type of processor or computer having correspondingly necessary peripherals (memory, input/output interfaces, input/output devices, etc).
- the explanations above relating to the method apply to the apparatus accordingly.
- the apparatus may be embodied in one component or in distributed fashion in a plurality of components.
- a portion of the apparatus may be linked via a network interface (e.g. the Internet).
- the object is achieved by proposing a system or a computer network comprising at least one of the apparatuses described here.
- the solution presented herein also comprises a computer program product that can be loaded directly into a memory of a digital computer, comprising program code portions that are suitable for carrying out steps of the method described herein.
- a computer-readable storage medium e.g. an arbitrary memory, comprising instructions (e.g. in the form of program code) that can be executed by a computer and that are suited to the computer carrying out steps of the method described here.
- FIG. 1 shows a schematic illustration of an algorithm in pseudo-code notation to provide an exemplary explanation of the propagation of the labels on the basis of rule (CR4);
- FIG. 2 shows a schematic block diagram with steps of the method proposed herein
- FIG. 3 shows a schematic block diagram with control units for actuating a technical installation.
- the solution proposed here comprises particularly the following steps:
- model-based information interpretation (and hence also of model-based diagnosis) is significantly extended by the approach proposed here, since it is now also possible to process situations with an abundance of observation data (or a defectively formulated model).
- the demonstrated solution is conservative, i.e. in cases in which a conventional method delivers a solution, a corresponding solution is also provided by the approach proposed here.
- ⁇ ⁇ ⁇ ⁇ can be understood as an inversion of the modus ponens rule that allows ⁇ to be derived as a hypothetical explanation for the occurrence of ⁇ , under the assumption that the presence of ⁇ in some sense justifies ⁇ .
- the present case deals with logic-based abduction over ⁇ + TBoxes.
- other logic-based presentation schemes are also possible.
- an abduction problem does not have a single solution but rather has a collection of alternative answers A 1 . . . A 2 , . . . A k , from among which optimal solutions are selected by means of an order of preference “ ⁇ ”.
- the expression A i ⁇ A j denotes that A i is “not worse” that “A j ”, with an indifference A i ⁇ A j A j ⁇ A i , where A i ⁇ A j and a strict preference A i ⁇ A j A j ⁇ A i where A i ⁇ A j being determined. It is then possible for a (normal) preference-based abduction problem to be defined as follows:
- Typical orders of preference over sets are or comprise
- the first two orders of preference give preference to a set A over any of its subsets; this monotonicity property is formalized in definition 2 below.
- abductive information interpretation using a formal domain model include media interpretation and diagnostics for complex technical systems such as production machines. These domains have many, in some cases simple, observations on account of a large number of sensors, whereas the model for all of these observations is often inadequately or incompletely specified.
- the following example illustrates how the classical definition of abduction can fail in a specific situation:
- a production system comprises a diagnosis unit, wherein the production system has been mapped using a model.
- the model indicates that a fluctuating supply of current is manifested by intermittent failures in a main control unit, while the communication links remain operational and a mechanical gripper in the production system is unaffected (the observations are deemed to be modeled as a causal consequence of the diagnosis).
- the order ⁇ A is chosen to be monotone and the order ⁇ O is chosen to be anti-monotone for subset relationships.
- the next section provides an approach in order to solve a relaxed abduction. This approach is based on the simultaneous optimization of ⁇ A and ⁇ O .
- ⁇ + is a member of the ⁇ family, for which a subsumption can be verified in PTIME.
- ⁇ + axioms are
- a 1 ⁇ B ( NF1 ) A 1 ⁇ A 2 ⁇ B ( NF2 ) A 1 ⁇ ⁇ r ⁇ B ( NF3 ) ⁇ r ⁇ A 2 ⁇ B ( NF4 ) r 1 ⁇ s ( NF5 ) r 1 ⁇ r 2 ⁇ s ( NF6 ) for A 1 , A 2 , B ⁇ N C T N C ⁇ T ⁇ and r 1 , r 2 , s ⁇ N R .
- (NF1) describes a concept inclusion “all objects in a class A 1 are also objects in a class B”.
- NF2 describes: “if an object belongs to class A 1 and to class A 2 then it also belongs to class B”. This can be shortened to “A 1 and A 2 are implied by B”.
- (NF3) denotes: “if an object belongs to class A 1 then it is linked to at least one object in class B via a relation r”.
- (NF4) describes: “if an object is linked to at least one object in class A 2 by means of a relation r then said object belongs to class B”.
- the normal forms (NF5) and (NF6) are obtained accordingly for the roles r 1 , r 2 , s ⁇ N R .
- the ⁇ family allows a completion-based reasoning scheme that explicitly derives valid subsumptions, specifically using a set of rules in the style of Gentzen's sequent calculus (also called “Gentzen calculus”).
- a graph structure which is produced using the rules allows subsumptions to be derived.
- the axiom-oriented representation allows a high level of flexibility and reuse of information.
- any normalized axiom set can accordingly be mapped as a hypergraph (or as an appropriate representation of such a hypergraph), the nodes of which are axioms of type (NF1) and (NF3) over the concepts and the role names that are used in the axiom set (in line with all statements that are admissible as a premise or conclusion in a derivation step).
- Hyperedges of the hypergraph are induced by transpositions of the rules (CR1) to (CR6); by way of example, an instantization of the rule (CR4), which derives C F from C ⁇ r.D and D E using the axiom ⁇ r.E F, induces a hyperedge ⁇ C ⁇ r.D,D E ⁇ C F.
- Elements from the set of all observations O represent information that is to be justified (i.e. that is derived), and therefore correspond to nodes of the hypergraph.
- the hyperedges are provided with a label on the basis of this criterion. This is also evident from the definition below.
- H RAP is bounded polynomially in
- Checking whether a concept inclusion D E(C ⁇ r.D) can be derived also checks whether the graph contains a hyperpath from V T to the node D E(C ⁇ r.D).
- hyperpath there is a hyperpath from X to t if there is a hyperedge that connects a particular set of nodes Y to t, and each y i ⁇ Y
- This section provides an exemplary explanation of an algorithm for solving the relaxed abduction problem RAP. This involves determining the shortest hyperpaths by taking into account two different criteria (multi-aim optimization).
- the join operator can be defined as follows:
- FIG. 1 shows a schematic illustration of an algorithm in pseudo code notation for the exemplary explanation of the propagation of the labels on the basis of the rule (CR4).
- the algorithm shown in FIG. 1 is used to produce the labels for the hyperpath of the relaxed abduction problem.
- initialization takes place and in the subsequent lines of the code fragment shown, the labels are assigned and alterations to the labels are propagated.
- the present approach provides an opportunity for relaxed abduction for a description logic.
- Relaxed abduction extends logic-based abduction by the option of interpreting incorrect information for incomplete models.
- a solution to relaxed abduction over ⁇ + knowledge bases is presented on the basis of pareto-optimal hyperpaths in the derivation graph. The performance of this approach also has critical advantages over that of mere enumeration despite the inherent exponential growth of node labels.
- the proposed algorithm can accordingly be applied to other description logics for which it is possible to determine subsumption by means of completion. This is the case for the ⁇ ++ description logic, for example.
- FIG. 2 shows a schematic block diagram with steps of the method proposed herein:
- a relaxed abduction problem is determined for the technical system, e.g. on the basis of data from measurement pickups or sensors or other capturable data relating to the technical system.
- the relaxed abduction problem is solved by determining tuples that are optimal with respect to two preference orders over subsets of assumptions and observations, respectively, while concurrently minimizing the subset of assumptions to explain the observations and maximize a consistency of the observations with the solution 203 .
- the technical system is actuated according to the solution of the relaxed abduction problem.
- the technical system may be a technical installation, assembly, process monitoring, a power station or the like.
- FIG. 3 shows a schematic block diagram with a control unit 301 that is arranged by way of example within a technical installation 302 .
- a control unit 303 is provided, which is arranged separately from the technical installation 302 and is connected thereto via a network 304 , for example the Internet.
- Both control units 301 , 303 can be used in order to actuate the technical system 302 ; in particular, it is possible for at least one of the control units 301 , 303 to carry out diagnosis for the technical system 302 and/or to set parameters for the technical system 302 .
Abstract
To enable efficient abduction even for observations that are faulty or inadequately modeled, a relaxed abduction problem is proposed in order to explain the largest possible part of the observations with as few assumptions as possible. On the basis of two preference orders over a subset of observations and a subset of assumptions, tuples can therefore be determined such that the theory, together with the subset of assumptions, explains the subset of observations. The formulation as a multi-criteria optimization problem eliminates the need to offset assumptions made and explained observations against one another. Due to the technical soundness of the approach, specific properties of the set of results (such as correctness, completeness etc.), can be checked, which is particularly advantageous in safety-critical applications. The complexity of the problem-solving process can be influenced and therefore flexibly adapted in terms of domain requirements through the selection of the underlying representation language and preference relations. The invention can be applied to any technical system, e.g. plants or power stations.
Description
The present application is a 35 U.S.C. §371 national phase application based on PCT/EP2012/062815, filed Jul. 2, 2012, which claims priority of German Patent Application No. 10 2011 079 034.9, filed Jul. 12, 2011, the contents of both of which are incorporated in full by reference herein.
The invention relates to a method and an apparatus for actuating a technical system.
Model-based information interpretation (and the application thereof within the framework of model-based diagnosis) is becoming increasingly important. In this context, model-based methods have the advantage of an explicit and comprehensible description of the domain (e.g. of the technical system requiring a diagnosis). Such an explicit model can be examined and understood, which promotes acceptance by the user, particularly in respect of a diagnosis or an interpretation result. In addition, the models can be customized for new machines, extended by new domain knowledge and, depending on the type of presentation, even checked for correctness with reasonable effort. It is also possible to use a vocabulary of the model for man-machine interaction and hence for implementing an interactive interpretation process.
In the case of a logic-based representation of the domain model, the interpretation process is frequently implemented by means of what is known as (logic-based) abduction. This is an attempt to explain the observed information (such as sensor measurements and results from preprocessing processes) by using a formal model. In this context, allowance is made for the fact that the set of observations (e.g. owing to measurement inaccuracies, absence of sensors, etc.) is often incomplete by being able to assume missing information during an explanatory process. In formal terms, the object is thus to determine, for a given model T (also called the “theory”) and a set of observations O, a set A of assumptions (usually as a subset A⊂A from all possible assumptions A) such that the observations O are explained by the model T and also the assumptions A⊂A. In this case, the problem is worded as an optimization problem, i.e. the “best” such set A⊂A of assumptions is sought (according to the optimality criterion, e.g. the smallest set, or the set with the lowest weight).
In the practice of automatic information interpretation and/or diagnosis, there is—besides the problem of missing observations—also the problem that observations exist that cannot be explained with the given model. Typical causes of this are, by way of example, faulty sensors that deliver measured values outside an envisaged range, or else incomplete models that do not take account of at least one arising combination of observations. Such problems clearly restrict the practical usability of abduction-based information interpretation.
The object of the invention is to avoid the disadvantages cited above and to allow an opportunity for abduction even in the case of erroneous observations.
The object is achieved by proposing a method for actuating a technical system,
-
- in which a relaxed abduction problem is determined,
- in which the relaxed abduction problem is solved and the technical system is actuated as appropriate.
In this context, it should be noted that the actuation may relate to or comprise control, diagnosis or other processing of data from the technical system. In particular, the actuation in this case also comprises diagnosis, for example pertaining to the use of the information during a maintenance interval.
As a result of the wording as a multicriterion optimization problem, there is no longer the need to offset assumptions made and observations explained against one another.
The presented approach is highly generic, i.e. it does not require any assumptions about the preference relations used besides the intuitive stipulation that the addition of a further assumption (in the case of an unaltered observation set) cannot improve the preference and the addition of an explained observation (in the case of an unaltered assumption set) cannot impair the preference.
On account of the formal soundness of the approach, it is possible for particular properties of the result set (such as correctness, completeness, etc.) to be checked and substantiated, which is advantageous particularly in safety-critical applications.
Using the choice of underlying representational language and of preference relations, it is possible for the complexity of the problem solving process to be influenced and thus customized to any domain requirements.
One development is that two orders of preference over a subset of the observations and a subset of the assumptions are taken as a basis for determining tuples, so that the theory together with the subset of the assumptions explains the subset of the observations.
This formalizes the intuitive approach of explaining the largest possible portion of observations seen with as few assumptions as possible; in this case, optimality corresponds to pareto-optimality for the two orders of preference (since maximization of the observations and minimization of the assumptions are opposite or different aims). A solution to the problem consists of pareto-optimal pairs (A,O).
The general definition—based on general orders—of the optimality allows the use of various optimality terms, for example minimum and/or maximum number of elements, subset and/or superset relationship, or minimum and/or maximum sum of the weights of the elements contained.
Another development is that the relaxed abduction problem is determined to be RAP=(T,A,O,≦A,≦O),
wherein
-
- the theory T,
- a set of abducible axioms A,
- a set O of observations,
with - TO and
- the orders of preference
≦A ⊂P(A)×P(A) and
≦O ⊂P(O)×P(O)
are taken as a basis for determining ≦-minimal tuples (A,O)εP(A)×P(O) so that T∪A is consistent and T∪A|=O holds.
In this case, the order ≦ is based on the orders ≦A and ≦O as follows:
(A,O)≃(A′,O′) A≃ A A′ O≃ O O′
(A,O)<(A′,O′)(A≦ A A′ O< O O′)(A< A A′ O≦ O O′)
(A,O)≦(A′,O′)((A,O)<(A′,O′))((A,O)≃(A′,O′))
(A,O)≃(A′,O′) A≃ A A′ O≃ O O′
(A,O)<(A′,O′)(A≦ A A′ O< O O′)(A< A A′ O≦ O O′)
(A,O)≦(A′,O′)((A,O)<(A′,O′))((A,O)≃(A′,O′))
Hence, it is proposed that incorrect and missing information are two complementary facets of defective information and are therefore handled in the same way. In addition to the prerequisite that a required piece of information is based on a set of the assumptions A (also referred to as: abducibles or abducible axioms), the relaxed abduction ignores observations from the set O during production of hypotheses if required.
Accordingly, a good solution has a high level of significance for the observations while being based on assumptions as little as possible. Therefore, advantageously, the order ≦A is chosen to be monotone and the order ≦O is chosen to be anti-monotone for subset relationships.
In particular, it is a development that the relaxed abduction problem is solved by transforming the relaxed abduction problem into a hypergraph, so that tuples (A,O) are encoded by pareto-optimal paths in the hypergraph.
It is also a development that the pareto-optimal paths are determined by means of a label approach.
In addition, it is a development that hyperedges of the hypergraph are induced by transcriptions of prescribed rules.
A subsequent development is that the prescribed rules are determined as follows:
One embodiment is that a weighted hypergraph HRAP=(V,E) that is induced by the relaxed abduction problem is determined by
V={(A B),(A ∃r.B)|A,BεN C T ,rεN R},
wherein
V T={(A A),(A T)|AεN C T }⊂V
denotes a set of final states and E denotes a set of the hyperedges
e=(T(e),h(e),w(e)),
so that the following holds: there is an axiom aεT∪A| that justifies the derivation h(e)εV from T(e)⊂V on the basis of one of the prescribed rules, wherein the edge weight w(e) is determined according to
V={(A B),(A ∃r.B)|A,BεN C T ,rεN R},
wherein
V T={(A A),(A T)|AεN C T }⊂V
denotes a set of final states and E denotes a set of the hyperedges
e=(T(e),h(e),w(e)),
so that the following holds: there is an axiom aεT∪A| that justifies the derivation h(e)εV from T(e)⊂V on the basis of one of the prescribed rules, wherein the edge weight w(e) is determined according to
An alternative embodiment is that pX,t=(VX,t,EX,t) is determined as a hyperpath in H=(V,E) from X to t if
-
- (1) tεX and pX,t=({t},∅) or
- (2) there is an edge eεE|, so that
- h(e)=t, T(e)={y1 . . . yk} holds.
In this case, pX,y i | are hyperpaths from X to yi:
V⊃V X,t ={t}∪∪ yi εT(e) V X,y i ,
E⊃E X,t ={e}∪∪ yi εT(e) E X,y i .
V⊃V X,t ={t}∪∪ y
E⊃E X,t ={e}∪∪ y
A subsequent embodiment is that shortest hyperpaths are determined by taking account of two preferences.
It is also an embodiment that the shortest hyperpaths are determined by taking account of two preferences by means of a label correction algorithm.
One development is that the labels encode pareto-optimal paths to the hitherto found nodes of the hypergraph.
An additional embodiment is that alterations along the hyperedges are propagated by means of a meet operator and/or by means of a join operator.
Another embodiment is that the relaxed abduction problem is determined by means of a piece of description logic.
The above object is also achieved by means of an apparatus for actuating a technical system comprising a processing unit that is set up such that
-
- a relaxed abduction problem can be determined,
- the relaxed abduction problem can be solved and the technical system can be actuated as appropriate.
The processing unit may, in particular, be a processor unit and/or an at least partially hardwired or logic circuit arrangement that, by way of example, is set up such that the method as described herein can be carried out. Said processing unit may be or comprise any type of processor or computer having correspondingly necessary peripherals (memory, input/output interfaces, input/output devices, etc).
The explanations above relating to the method apply to the apparatus accordingly. The apparatus may be embodied in one component or in distributed fashion in a plurality of components. In particular, it is also possible for a portion of the apparatus to be linked via a network interface (e.g. the Internet).
In addition, the object is achieved by proposing a system or a computer network comprising at least one of the apparatuses described here.
The solution presented herein also comprises a computer program product that can be loaded directly into a memory of a digital computer, comprising program code portions that are suitable for carrying out steps of the method described herein.
In addition, the aforementioned problem is solved by means of a computer-readable storage medium, e.g. an arbitrary memory, comprising instructions (e.g. in the form of program code) that can be executed by a computer and that are suited to the computer carrying out steps of the method described here.
The properties, features and advantages of this invention that are described above and also the manner in which they are achieved will become clearer and more distinctly comprehensible in connection with the schematic description of exemplary embodiments that follows, these being explained in more detail in connection with the drawings. In this case, elements that are the same or that have the same action may be provided with the same reference symbols for the sake of clarity.
The solution proposed here comprises particularly the following steps:
- (1) The definition of the logic-based abduction is formally relaxed so as to obtain important properties of defined problems (such as the verifiability of statements about correctness and existence of solutions, etc).
- In particular, a relaxed abduction problem (see below: definition 3) is determined. On the basis of two orders of preference over sets of observations or assumptions, “optimal” pairs (also referred to as tuples) (A,O) (with A⊂A, O⊂O) are now intended to be determined, so that the theory T together with the set of assumptions A⊂A explains the observations O⊂O, formally: T∪A|=O.
- This formalizes the intuitive approach of explaining the largest possible portion of the observations seen with as few assumptions as possible; in this case, optimality corresponds to pareto-optimality for the two orders of preference (since maximization of the observations and minimization of the assumptions are opposite or different aims). A solution to the problem consists of all pareto-optimal pairs (A,O).
- The general definition—based on general orders—of the optimality allows the use of various optimality terms, for example minimum and/or maximum number of elements, subset and/or superset relationship, or minimum and/or maximum sum of the weights of the elements contained.
- (2) In addition, it is proposed that the specified relaxed abduction problem be solved in a suitable manner. In this context, the relaxed abduction problem is translated into a hypergraph such that optimal pairs (A,O) are encoded by pareto-optimal paths in the induced hypergraph. The optimum paths are determined by using a label approach.
Taken together, these two steps allow solutions to an interpretation problem to be found even when it is not possible to explain all observations.
Overall, the field of application of model-based information interpretation (and hence also of model-based diagnosis) is significantly extended by the approach proposed here, since it is now also possible to process situations with an abundance of observation data (or a defectively formulated model). In this case, the demonstrated solution is conservative, i.e. in cases in which a conventional method delivers a solution, a corresponding solution is also provided by the approach proposed here.
Relaxed abduction with a solution is described in detail below.
Although abductive reasoning over principles of description logic knowledge is applied successfully to various information interpretation processes, it cannot provide adequate (or even any) results if it is confronted by incorrect information or incomplete models. The relaxed abduction proposed here solves this problem by ignoring incorrect information, for example. This can be done automatically on the basis of joint optimization of the sets of explained observations and required assumptions. By way of example, a method is presented that solves the relaxed abduction over εζ+ TBoxes based on the notion of shortest hyperpaths with multiple criteria.
Abduction was introduced in the late 19th century by Charles Sanders Pierce as an inference scheme aimed at deriving potential explanations for a particular observation. The rule formulated in this context
can be understood as an inversion of the modus ponens rule that allows φ to be derived as a hypothetical explanation for the occurrence of ω, under the assumption that the presence of φ in some sense justifies ω.
This general formulation cannot presuppose any causality between φ and ω in this case. Various notions of how φ justifies the presence of ω give rise to different notions of abductive inference, such as what is known as a set-cover-based approach, logic-based approaches or a knowledge-level approach.
In particular, the present case deals with logic-based abduction over εζ+ TBoxes. Correspondingly, other logic-based presentation schemes are also possible.
On account of its hypothetical nature, an abduction problem does not have a single solution but rather has a collection of alternative answers A1 . . . A2, . . . Ak, from among which optimal solutions are selected by means of an order of preference “<”. The expression
A i ≦A j
denotes that Ai is “not worse” that “Aj”, with an indifference
A i ≦A j A j ≦A i, where A i ≃A j
and a strict preference
A i ≦A j A j ≃A i where A i <A j
being determined. It is then possible for a (normal) preference-based abduction problem to be defined as follows:
A i ≦A j
denotes that Ai is “not worse” that “Aj”, with an indifference
A i ≦A j A j ≦A i, where A i ≃A j
and a strict preference
A i ≦A j A j ≃A i where A i <A j
being determined. It is then possible for a (normal) preference-based abduction problem to be defined as follows:
Definition 1: Preference-Based Abduction Problem
PAP=(T,A,O,≦ A)
PAP=(T,A,O,≦ A)
-
- In view of a set of axioms T, referred as the “theory”, a set of abducible axioms A, a set O of axioms that represent observations, so that T|≠O holds, and a (not necessarily total) order relationship
≦A ⊂P(A)×P(A)|, - all ≦A-minimal sets A⊂A are determined, so that T∪A is consistent and T∪A|=O holds.
- In view of a set of axioms T, referred as the “theory”, a set of abducible axioms A, a set O of axioms that represent observations, so that T|≠O holds, and a (not necessarily total) order relationship
Typical orders of preference over sets are or comprise
-
- subset minimality,
A i≦s A j A i ⊂A j, - minimal cardinality
A i≦c A j |A i |≦|A j| or - weighting-based orders, which are defined by a function ω which assigns numerical weights to subsets of A
A i≦w A j w(A i)≦w(A j).
- subset minimality,
The first two orders of preference give preference to a set A over any of its subsets; this monotonicity property is formalized in definition 2 below.
Definition 2: monotone and anti-monotone order
-
- An order ≦(<) over sets is monotone (strictly monotone) for a subset relationship if S′⊂S implies S′≦S (or S′⊂S implies S′<S).
- Conversely, an order ≦(<) is anti-monotone (strictly anti-monotone) for a subset relationship if S′⊃S implies S′≦S (S′⊃S implies S′<S).
Applications of abductive information interpretation using a formal domain model include media interpretation and diagnostics for complex technical systems such as production machines. These domains have many, in some cases simple, observations on account of a large number of sensors, whereas the model for all of these observations is often inadequately or incompletely specified. The following example illustrates how the classical definition of abduction can fail in a specific situation:
A production system comprises a diagnosis unit, wherein the production system has been mapped using a model. The model indicates that a fluctuating supply of current is manifested by intermittent failures in a main control unit, while the communication links remain operational and a mechanical gripper in the production system is unaffected (the observations are deemed to be modeled as a causal consequence of the diagnosis).
It is now assumed that a new additional vibration sensor observes low-frequency vibrations in the system. If the diagnostic model has not yet been extended in respect of this vibration sensor, which means that the observations of the vibration sensor also cannot be taken into account, the low-frequency vibrations delivered by the vibration sensor will unsettle the diagnostic process and prevent effective diagnosis in relation to the supply of current, even though the data delivered by the vibration sensor could actually be totally irrelevant.
Hence, the extension of the system by the vibration sensor results in the diagnosis no longer working reliably.
This flaw is based—according to the above definition of the preferred abduction problem—on the need for an admissible solution to have to explain every single observation oiεO|. This severely restricts the practical applicability of logic-based abduction to real industry applications in which an ever greater number of sensor data items produce and provide information that is not (yet) taken into account by the model.
An extension of logic-based abduction is therefore proposed below, so that even a wealth of data provide the desired results, e.g. diagnoses, flexibly and correctly.
Relaxed Abduction
Whereas, for simple models, it is still possible for incorrect information to be identified and possibly removed in a preprocessing step with a reasonable amount of effort, this is not possible for many real and correspondingly complex models, also because the relevance of a piece of information is dependent on the interpretation thereof and hence is not known in advance.
Hence, it is proposed that incorrect and missing information are two complementary facets of defective information and are therefore handled in the same way. In addition to the prerequisite that a required piece of information is based on the set of the assumptions A (also referred to as: abducibles or abducible axioms), the relaxed abduction ignores observations from the set O during production of hypotheses if required. This is formalized in definition 3.
Definition 3: Relaxed Abduction Problem
RAP=(T,A,O,≦ A,≦O)
RAP=(T,A,O,≦ A,≦O)
-
- On the basis of a set of axioms T, referred to as the “theory”, a set of abducible axioms A, a set O of axioms that represent observations, so that T|≠O holds, and two (not necessarily total) order relationships
≦A ⊂P(A)×P(A) and
≦O ⊂P(O)×P(O), - all ≦-minimal tuples
(A,O)εP(A)×P(O) - are determined, so that T∪A is consistent and T∪A|=O holds.
- In this case, the order <| is based on the orders ≦A and ≦O as follows:
(A,O)≃(A′,O′) A≃ A A′ O≃ O O′
(A,O)<(A′,O′)(A≦ A A′ O< O O′)(A< A A′ O≦ O O′)
(A,O)≦(A′,O′)((A,O)<(A′,O′))(A,O)≃(A′,O′))
- On the basis of a set of axioms T, referred to as the “theory”, a set of abducible axioms A, a set O of axioms that represent observations, so that T|≠O holds, and two (not necessarily total) order relationships
Accordingly, a good solution has a high level of significance for the observations while being based on assumptions as little as possible. Therefore, advantageously, the order ≦A is chosen to be monotone and the order ≦O is chosen to be anti-monotone for subset relationships.
Using inclusion as an order criterion over sets, the following will hold:
A≦ A A′ A⊂A′ and
O≦ O O′ O⊃O′.
A≦ A A′ A⊂A′ and
O≦ O O′ O⊃O′.
For the example cited above with the augmented vibration sensor, a minimal solution that explains all observations apart from the vibrations is obtained on the basis of the order. Therefore, this vibration is not taken into account in the diagnosis, which allows the fluctuating supply of current to be indicated as the result of the diagnosis.
Assertion 1: Conservativeness:
-
- A⊂A is a solution for the preference-based abduction problem PAP=(T,A,O,≦A) if (A,O) is a solution to the relaxed abduction problem RAP=(T,A,O,≦A,≦O), specifically for any order ≦O, which is anti-monotone for the subset relationship.
Evidence:
-
- It is assumed that A solves the preferred abduction problem PAP=(T,A,O,≦A). The following then holds:
- T∪A is consistent
- T∪A|=O and
- A is ≦A-minimal.
- Since the order ≦O for the subset relationship is anti-monotone, O is also ≦O-minimal; (A,O) is therefore ≦-minimal and hence solves the relaxed abduction problem RAP.
- Conversely, the following holds: if (A,O) solves the relaxed abduction problem RAP, then the following holds:
- T∪A is consistent
- T∪A|=O and
- (A,O) is ≦-minimal.
- If it is assumed that A≦AA′ holds, so that it follows that: A⊂A′, T∪A′ is consistent and T∪A′|=O, then it holds that: (A′,O)<(A,O), which is inconsistent with the ≦-minimality of (A,O).
- It is assumed that A solves the preferred abduction problem PAP=(T,A,O,≦A). The following then holds:
Conservativeness states that under ordinary circumstances relaxed abduction provides all solutions (provided that there are some) to the corresponding standard abduction problem (i.e. the nonrelaxed abduction problem). Since the ≦A-order and the ≦O-order are typically competing optimization aims, it is expedient to treat relaxed abduction as an optimization problem with two criteria. ≦-Minimal solutions then correspond to pareto-optimal points in the space of all combinations (A,O) that meet the logical requirements of a solution (consistency and explanation of the observations).
Assertion 2: Pareto-Optimality of RAP:
-
- Let RAP=(T,A,O,≦A,≦O) be a relaxed abduction problem. (A*,O*) is a solution to the relaxed abduction problem RAP if it is a pareto-optimal element (on the basis of the orders ≦A and ≦O) in the solution space
{(A,O)εP(A)×P(O)|T∪A|=O T∪A|≠⊥}.
- Let RAP=(T,A,O,≦A,≦O) be a relaxed abduction problem. (A*,O*) is a solution to the relaxed abduction problem RAP if it is a pareto-optimal element (on the basis of the orders ≦A and ≦O) in the solution space
Evidence:
-
- If (A*,O*) solves the relaxed abduction problem RAP, then it holds that:
- T∪A* is consistent and
- T∪A*|=O*.
- (A*,O*) is therefore an element of the explanation space (ES); in addition, (A*,O*) is ≦-minimal.
- It is now assumed that (A*,O*) is not pareto-optimal for ES, and also that (A′,O′)εES, so that (without loss of generality) A′<AA* and O′<OO* hold.
- This would result in (A′,O′)<(A*,O*).
- which would be inconsistent with ≦-minimality of (A*,O*). Hence, (A*,O*) is a pareto-optimal element of the explanation space ES.
- Similarly, (A′,O′) is a pareto-optimal element of the explanation space ES. In order to show that the tuple is ≦-minimal, let (A*,O*) be a solution to a relaxed abduction problem RAP, so that the following holds:
(A*,O*)<(A′,O′) - Without loss of generality, this gives A*<AA′ and O*<OO′, which is inconsistent with the pareto-optimality of (A′,O′). Therefore, (A′,O′) must be ≦-minimal and hence solves the relaxed abduction problem RAP.
- If (A*,O*) solves the relaxed abduction problem RAP, then it holds that:
The next section provides an approach in order to solve a relaxed abduction. This approach is based on the simultaneous optimization of ≦A and ≦O.
Solving Relaxed Abduction
The description logic εζ+ is a member of the εζ family, for which a subsumption can be verified in PTIME. εζ+ concept descriptions are defined by
C::=T|A|C C|∃r.C
(where AεNC is a concept name and rεNR is a role name). εζ+ axioms are
C::=T|A|C C|∃r.C
(where AεNC is a concept name and rεNR is a role name). εζ+ axioms are
-
- concept inclusion axioms CD or
- role inclusion axioms r1 ∘ . . . ∘ rk r
with C, D concept descriptions; r, r1 . . . , rkεNR, k≧1. In this case, NC denotes the set of concept names and NR denotes the set of role names.
Since any εζ+ TBox can be normalized with only a linear increase in magnitude, it holds that all axioms have one of the following (normal) forms:
for A1, A2, BεNC T=NC∪{T} and r1, r2, sεNR.
Accordingly, (NF1) describes a concept inclusion “all objects in a class A1 are also objects in a class B”. (NF2) describes: “if an object belongs to class A1 and to class A2 then it also belongs to class B”. This can be shortened to “A1 and A2 are implied by B”. (NF3) denotes: “if an object belongs to class A1 then it is linked to at least one object in class B via a relation r”. Accordingly, (NF4) describes: “if an object is linked to at least one object in class A2 by means of a relation r then said object belongs to class B”. The normal forms (NF5) and (NF6) are obtained accordingly for the roles r1, r2, sεNR.
In addition to standard refutation-based table reasoning, the εζ family allows a completion-based reasoning scheme that explicitly derives valid subsumptions, specifically using a set of rules in the style of Gentzen's sequent calculus (also called “Gentzen calculus”).
The rules (completion rules CR and initialization rules IR) are presented below:
A graph structure which is produced using the rules allows subsumptions to be derived.
By way of example, it is assumed that both the set of assumptions A and the set of observations O, like the theory T, are axioms of the description logic.
The axiom-oriented representation allows a high level of flexibility and reuse of information.
From Completion Rules to Hypergraphs
Since the rules shown above are a complete evidence system for εζ+, any normalized axiom set can accordingly be mapped as a hypergraph (or as an appropriate representation of such a hypergraph), the nodes of which are axioms of type (NF1) and (NF3) over the concepts and the role names that are used in the axiom set (in line with all statements that are admissible as a premise or conclusion in a derivation step).
Hyperedges of the hypergraph are induced by transpositions of the rules (CR1) to (CR6); by way of example, an instantization of the rule (CR4), which derives CF from C∃r.D and DE using the axiom ∃r.EF, induces a hyperedge
{C ∃r.D,D E}→C F.
{C ∃r.D,D E}→C F.
This correspondence can also be extended to relaxed abduction problems as follows: Both T and A contain arbitrary εζ+ axioms in normal form that can justify individual derivation steps represented by a hyperedge (in order to simplify the representation, it can be assumed that A∩T=∅ holds).
Elements from the set of all observations O, on the other hand, represent information that is to be justified (i.e. that is derived), and therefore correspond to nodes of the hypergraph. This requires axioms from O to be only of type (NF1) and (NF3); this is a restriction that is usable in practice, since (NF2) axioms and (NF4) axioms can be converted into an (NF1) axiom, specifically using a new concept name, and since role inclusion axioms are not needed in order to express observations about domain objects. Preferably, the hyperedges are provided with a label on the basis of this criterion. This is also evident from the definition below.
Definition 4: Induced Hypergraphs HRAP:
-
- Let RAP=(T,A,O,≦A,≦O) be a relaxed abduction problem. A weighted hypergraph HRAP=(V,E), which is induced by RAP, is defined by
V={(A B),(A ∃r.B)|A,BεN C T ,rεN R}|,
V T={(A A),(A T)|AεN C T }⊂V - denotes the set of final states and E denotes the set of all hyperedges
e=(T(e),h(e),w(e)), - so that the following holds:
- There is an axiom aεT∪A that justifies the derivation h(e)εV from T(e)⊂V on the basis of one of the rules (CR1) to (CR6). The edge weight w(e) is defined by
- Let RAP=(T,A,O,≦A,≦O) be a relaxed abduction problem. A weighted hypergraph HRAP=(V,E), which is induced by RAP, is defined by
In this context, it should be noted that the magnitude of HRAP is bounded polynomially in |NC| and |NR|. Checking whether a concept inclusion DE(C∃r.D) can be derived also checks whether the graph contains a hyperpath from VT to the node DE(C∃r.D).
Intuitively, there is a hyperpath from X to t if there is a hyperedge that connects a particular set of nodes Y to t, and each yiεY| can be reached from X via a hyperpath. This is formalized using the definition below.
Definition 5: Hyperpath:
-
- pX,t=(VX,t,EX,t) is a hyperpath in H=(V,E) from X to t| if
- (1) tεX and pX,t=({t},∅) or
- (2) there is an edge eεE, so that
- h(e)=t,T(e)={y1 . . . , yk} holds.
- In this case pX,y
i are hyperpaths from X to yi:
V⊃V X,t ={t}∪∪ yi εT(e) V X,yi ,
E⊃E X,t ={e}∪∪ yi εT(e) E X,yi .
Hyperpath Search for Relaxed Abduction
This section provides an exemplary explanation of an algorithm for solving the relaxed abduction problem RAP. This involves determining the shortest hyperpaths by taking into account two different criteria (multi-aim optimization).
Thus, an extended label correction algorithm for finding shortest paths using two criteria in a graph is proposed on the basis of [Skriver, A. J. V.: A classification of bicriterion shortest path (bsp) algorithms. Asia-Pacific Journal of Operational Research 17, pages 199-212 (2000)]. Thus, the graph is presented in a compact form using two lists S and R (see also: Baader, F., Brandt, S., Lutz, C.: Pushing the EL envelope. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence. Pages 364-369 (2005)). The entries in the list are extended by labels that encode the pareto-optimal paths to the previously found node. Alterations are propagated along the weighted edges using
-
- a meet operator ( operator) and
- a join operator ( operator).
In this case, the meet operator is defined as follows:
Function: meet (L1, L2, just concl) |
Input parameters: | L1 | Label set |
L2 | Label set | |
just | Axiom in normal form | |
concl | Axiom in normal form |
Output parameter: | Label set for the meet operator |
Result ← {A1 ∪ A2, O1 ∪ O2)|(A1, O1) L1, (A2, O2) ∈ L2}; |
if just ∈ A then result ← {(A ∪ {just},O)|(A, O) ∈ result}; |
if concl ∈ O then ←{(A, O) ∪ {concl}, O)|(A, O) ∈ result} |
return result; |
The join operator can be defined as follows:
Function: join (L1, L2) |
Input parameters | L1 | Label set |
L2 | Label set |
Output parameter | Label set for the join operator |
result ← L1 ∪ L2: |
result ← remove_dominated (result, A, O); |
return result: |
In this context, it should be noted that the “remove_dominated” functionality removes those labels that code relatively poor paths.
When saturation has been reached, the labels of all <-| minimal paths in HRAP are collected in the set
MP(H RAP):=∪vεVlabel(v).
MP(H RAP):=∪vεVlabel(v).
As already explained, the algorithm shown in FIG. 1 is used to produce the labels for the hyperpath of the relaxed abduction problem. In lines 1 to 4, initialization takes place and in the subsequent lines of the code fragment shown, the labels are assigned and alterations to the labels are propagated.
In line 7, all axioms a from T and A are selected in order and for each of these axioms a check is performed to determine whether the individual rules (CR1) to (CR6) apply. This is shown by way of example from line 8 onward for the rule (CR4). If need be, a new label L* is added in line 13 and a check is performed in line 14 to determine whether the label has been changed. If this is the case, the previous label entry is removed in line 15. Accordingly, the labels are added or updated.
In line 17, a check is performed to determine whether saturation has occurred, i.e. no further change is needed to be taken into account.
In this context, it should be noted that even though the order of propagations is irrelevant to correct ascertainment, it can have a significant effect on the number of candidates produced: finding almost optimal solutions may already result in a large number of less-than-optimal solutions in good time, which can be rejected. To improve performance, it is thus possible to use heuristics by first of all exhaustively applying propagations that are determined by elements of T and introducing assumptions only if such propagations are not possible.
Assertion 3: Correctness:
-
- The set of all solutions for a relaxed abduction problem RAP=(T,A,O,≦A,≦O) is indicated by a ≦|-minimal closure of MP(HRAP) under component-wise union as per
(A,O)(A′,O′):=(A∪A′,O∪O′).
- The set of all solutions for a relaxed abduction problem RAP=(T,A,O,≦A,≦O) is indicated by a ≦|-minimal closure of MP(HRAP) under component-wise union as per
Evidence:
-
- Hyperpaths in HRAP that begin at VT are derivations. Labels that are constructed on the basis of these hyperpaths can be used in order to encode relevant information that is used during this derivation. According to assertion 2, it is sufficient to show that the proposed algorithm correctly determines the labels for all pareto-optimal paths in HRAP that begin at VT.
- This can be verified inductively on the basis of the correctness of the meet and join operators. This closing synopsis of ∪vεVlabel(v) as a component-wise union is based on the insight that, since the two statements a and b have been verified, it is evidently possible to verify ab by combining the two items of evidence using the meet operator. In graphical terms, this can be regarded as addition of the associated node T, so that any other vεV is connected to the node T by means of a hyperedge ({v}, T, {∅,∅}). The label for this node then encodes all solutions to the relaxed abduction problem, and is calculated as indicated above.
Since the node labels can grow exponentially with the magnitude A and O, it is worthwhile, for general orders of preference such as the set inclusion, considering the advantage of the present method in comparison with a brute force approach: iteration is performed over all pairs (A,O)εP(A)×P(O), and all tuples (A,O) are collected, so that T∪A|=O holds; finally, all ≦-dominant tuples are eliminated. This approach requires 2|A|+|O| deducibility tests, with each set that passes this test being tested for ≦-minimality. The solution presented is superior to a brute force approach in several respects:
- a) in contrast to the uninformed brute force search outlined above, the approach proposed in this paper realizes an informed search as it does not generate all possible (A,O) pairs at random but rather only those for which the property T∪A|=O actually holds, without requiring any additional deducibility tests. The overall benefit of this property is dependent on the model of T and on the sets A and O. Problems that have only a few solutions therefore benefit most from the present proposal.
- b) Dropping ≦-dominated labels for ≦A and ≦O|, which are (anti-)monotone for set inclusion, reduces the worst case magnitude of node labels by at least a factor O(√{square root over (|A|·|O|)}).
- c) In addition to the upper limits for the magnitude of labels, it is also possible for the expected number of non-dominated paths to a state to be determined as follows: two arbitrary orders over elements of A and O are assumed, so that any subset can be encoded directly as a binary vector of length |A| or |O|. For this, it is possible to deduce that the labels grow on average only in the order of magnitude 1.5|A|+|O| instead of 2|A|+|O|.
Other selections for ≦A and ≦O| can lead to more considerable savings of computation effort, since the orders of preference are used as a pruning criterion while the solution is generated. This allows the present approach to be used for approximation.
If, by way of example, the assumption set and the observation set are compared not by means of set inclusion but rather by means of cardinality, the maximum label magnitude is decreased to |A|·|O|. This could—depending on the order of the rule application—not result in optimal solutions, however.
In a more complex design, e.g. for an installation or a technical system, it is possible to allocate numerical weights for observations and/or abducible axioms so that only such solutions as are substantially poorer than others are dropped. Alternatively, it is possible to use weights (or scores) in order to calculate limits for a maximum number of points that can be achieved by a partial solution; this number of points can be used as pruning criterion.
Hence, the present approach provides an opportunity for relaxed abduction for a description logic. Relaxed abduction extends logic-based abduction by the option of interpreting incorrect information for incomplete models. A solution to relaxed abduction over εζ+ knowledge bases is presented on the basis of pareto-optimal hyperpaths in the derivation graph. The performance of this approach also has critical advantages over that of mere enumeration despite the inherent exponential growth of node labels.
The proposed algorithm can accordingly be applied to other description logics for which it is possible to determine subsumption by means of completion. This is the case for the εζ++ description logic, for example.
The relaxed abduction described in the present case allows various specializations that are obtained from various selection options for ≦A and ≦O. By way of example, approximated solutions can be generated very efficiently (i.e. with a linear label magnitude) if set cardinality is used as a dominance criterion. It is also possible for the axioms to have weights allocated in order to allow early or even lossless pruning of less-than-optimum partial solutions; in this case, the label magnitudes are also reduced.
The technical system may be a technical installation, assembly, process monitoring, a power station or the like.
Although the invention has been illustrated and described in more detail using the at least one exemplary embodiment shown, the invention is not restricted thereto and other variations can be derived therefrom by a person skilled in the art without departing from the scope of protection of the invention.
Claims (20)
1. A method of actuating a technical system, the method comprising:
receiving at least one observation from at least one sensor;
determining, by a computer which is configured to execute program code stored on a non-transitory computer-readable medium, a relaxed abduction problem;
solving, by the computer, the relaxed abduction problem, by determining tuples that are optimal with respect to two preference orders over subsets of assumptions and observations, wherein the determined optimal tuples comprise a subset of observations smaller than a complete set of observations, respectively, that concurrently minimize the subset of assumptions to explain the observations comprising the at least one observation received from the at least one sensor and maximize the subset of observations abductively explained by the subset of assumptions given a theory T, with the objective of determining a causal consequence of the largest possible portion of observations with as few assumptions as possible; and
actuating the technical system according to the solution of the relaxed abduction problem by communicating at least one actuator signal.
2. The method as claimed in claim 1 , in which the relaxed abduction problem is determined to be RAP=(T,A,O,∘A,∘O),
wherein:
a set of abducible axioms is A,
a set of observations is O
with
T′/O; and
further comprising taking orders of preference
∘A ⊂P(A)×P(A) and
∘O ⊂P(O)×P(O)
∘A ⊂P(A)×P(A) and
∘O ⊂P(O)×P(O)
as a basis for determining ∘-minimal tuples (A,O)εP(A)×P(O),
so that T∪A is consistent and T∪A′O holds.
3. The method as claimed in claim 1 , wherein the relaxed abduction problem is solved by transforming the relaxed abduction problem into a hypergraph, so that the tuples (A,O) are encoded by pareto-optimal paths in the hypergraph.
4. The method as claimed in claim 3 , wherein the pareto-optimal paths are determined via a label approach.
5. The method as claimed in claim 3 , further comprising inducing hyperedges of the hypergraph by transcriptions of prescribed rules.
6. The method as claimed in claim 5 , wherein the prescribed rules are determined as follows:
7. The method as claimed in claim 3 , wherein a weighted hypergraph HRAP=(V,E) which is induced by the relaxed abduction problem, is determined by
V={(AôB),(A∃r.B)|A,BεN C T ,rεN R},
V={(AôB),(A∃r.B)|A,BεN C T ,rεN R},
wherein
V T={(AôA),(AôT)|AεN C 1 }⊂V
V T={(AôA),(AôT)|AεN C 1 }⊂V
denotes a set of final states and E denotes a set of the hyperedges
e=(T(e),h(e),w(e)),
e=(T(e),h(e),w(e)),
so that the following holds: an axiom aεT∪A exists that justifies derivation h(e)εV from T(e)⊂V based on one of the prescribed rules,
wherein the edge weight w(e) is determined according to
8. The method as claimed in claim 7 , wherein pX,t=(VX,t,EX,t) is determined as a hyperpath in H=(V,E) from X to t if
(1) tεX and pX,t=({t},0) or
(2) there is an edge eεE, so that h(e)=t,T(e)=(y1, . . . , yk) holds.
9. The method as claimed in claim 8 , wherein shortest hyperpaths are determined by taking account of two preferences.
10. The method as claimed in claim 9 , wherein the shortest hyperpaths are determined by taking account of the two preferences via a label correction algorithm.
11. The method as claimed in claim 10 , wherein the labels encode pareto-optimal paths to the hitherto found nodes of the hypergraph.
12. The method as claimed in claim 11 , wherein alterations along the hyperedges are propagated by a meet operator and/or by a join operator.
13. The method as claimed in claim 1 , wherein the relaxed abduction problem is determined via a piece of description logic.
14. A computer system for actuating a technical system, comprising:
a processor configured to automatically execute program code stored on a non-transitory computer-readable medium, to:
control receipt of at least one observation from at least one sensor;
determine a relaxed abduction problem;
solve the relaxed abduction problem by determining tuples that are optimal with respect to two preference orders over subsets of assumptions and observations, wherein the determined optimal tuples comprise a subset of observations smaller than a complete set of observations, respectively, that concurrently minimize the subset of assumptions to explain the observations comprising the at least one observation received from the at least one sensor and maximize the subset of observations abductively explained by the subset of assumptions given a theory T, with the objective of determining a causal consequence of the largest possible portion of observations with as few assumptions as possible; and
an actuator output port configured to actuate the technical system according to the solution of the relaxed abduction problem.
15. The computer as claimed in claim 14 , in which the relaxed abduction problem is determined to be:
RAP=(T,A,O,∘ A,∘O),
RAP=(T,A,O,∘ A,∘O),
wherein:
a set of abducible axioms is A,
a set of observations is O
with T′/O; and
further comprising taking orders of preference
∘A ⊂P(A)×P(A) and
∘O ⊂P(O)×P(O)
∘A ⊂P(A)×P(A) and
∘O ⊂P(O)×P(O)
as a basis for determining ∘-minimal tuples (A,O)εP(A)×P(O),
so that T∪A is consistent and T∪A′O holds.
16. The computer as claimed in claim 14 , wherein the processor is configured to solve the relaxed abduction problem by transforming the relaxed abduction problem into a hypergraph, so that the tuples (A,O) are encoded by pareto-optimal paths in the hypergraph.
17. The computer as claimed in claim 16 , wherein the processor is further configured to automatically induce hyperedges of the hypergraph by transcriptions of prescribed rules determined as follows:
18. The computer as claimed in claim 16 , wherein the processor is further configured to determine a weighted hypergraph HRAP=(V,E) induced by the relaxed abduction problem: V={(AôB),(A,∃r.B)|A,BεNC T,rεNR},
wherein:
VT={(AôA), (AôT)|AεNC 1}⊂V denotes a set of final states, and
E denotes a set of the hyperedges e=(T(e),h(e),w(e)),
so that the following holds: an axiom aεT∪A exists that justifies derivation h(e)εV from T(e)⊂V based on one of the prescribed rules,
wherein the edge weight w(e) is determined according to
19. A method of controlling a technical system, comprising:
receiving at least one observation from at least one sensor;
determining a relaxed abduction problem;
determining a pareto-optimum set of tuples (A,O) by taking as a basis two orders of preference over a subset of observations (O) smaller than a complete set of observations, and a subset of assumptions (A), so that a theory (T) together with a minimized subset of the assumptions (A) explains a maximized subset of the observations (O) comprising the at least one observation (O) received from the at least one sensor, with the objective of determining a causal consequence of the largest possible portion of subset of observations (O) with as few members of the subset of assumptions (A) as possible;
defining a solution to the determined relaxed abduction problem, by an automated computer which executes program code stored on a non-transitory computer-readable medium; and
actuating the technical system in accordance with the defined solution.
20. The method according to claim 19 , further comprising transforming the relaxed abduction problem into a hypergraph, so that the tuples (A,O) are encoded by pareto-optimal paths in the hypergraph.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20210326735A1 (en) * | 2018-08-27 | 2021-10-21 | Nec Corporation | Abduction apparatus, abduction method, and computer-readable recording medium |
US11436265B2 (en) * | 2017-06-13 | 2022-09-06 | Microsoft Technology Licensing, Llc | System for presenting tailored content based on user sensibilities |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102011079034A1 (en) | 2011-07-12 | 2013-01-17 | Siemens Aktiengesellschaft | Control of a technical system |
Citations (153)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4656603A (en) | 1984-03-01 | 1987-04-07 | The Cadware Group, Ltd. | Schematic diagram generating system using library of general purpose interactively selectable graphic primitives to create special applications icons |
US4783741A (en) | 1983-08-08 | 1988-11-08 | Bernhard Mitterauer | Computer system for simulating reticular formation operation |
US4813013A (en) | 1984-03-01 | 1989-03-14 | The Cadware Group, Ltd. | Schematic diagram generating system using library of general purpose interactively selectable graphic primitives to create special applications icons |
US5018075A (en) | 1989-03-24 | 1991-05-21 | Bull Hn Information Systems Inc. | Unknown response processing in a diagnostic expert system |
US5293323A (en) | 1991-10-24 | 1994-03-08 | General Electric Company | Method for fault diagnosis by assessment of confidence measure |
CN1092151A (en) | 1993-03-08 | 1994-09-14 | 三洋电机株式会社 | The control method of air conditioner |
US5631831A (en) | 1993-02-26 | 1997-05-20 | Spx Corporation | Diagnosis method for vehicle systems |
US5712960A (en) | 1993-07-02 | 1998-01-27 | Cv Soft, S.R.L. | System and methods for intelligent database management using abductive reasoning |
US5802256A (en) | 1994-05-09 | 1998-09-01 | Microsoft Corporation | Generating improved belief networks |
US5810747A (en) | 1996-08-21 | 1998-09-22 | Interactive Remote Site Technology, Inc. | Remote site medical intervention system |
US5812994A (en) | 1993-05-20 | 1998-09-22 | Canon Kabushiki Kaisha | Apparatus and method for data processing and/or for control |
US5852811A (en) | 1987-04-15 | 1998-12-22 | Proprietary Financial Products, Inc. | Method for managing financial accounts by a preferred allocation of funds among accounts |
US5870701A (en) | 1992-08-21 | 1999-02-09 | Canon Kabushiki Kaisha | Control signal processing method and apparatus having natural language interfacing capabilities |
US5884294A (en) | 1997-04-18 | 1999-03-16 | Northrop Grumman Corporation | System and method for functional recognition of emitters |
US6012152A (en) | 1996-11-27 | 2000-01-04 | Telefonaktiebolaget Lm Ericsson (Publ) | Software fault management system |
US6044347A (en) | 1997-08-05 | 2000-03-28 | Lucent Technologies Inc. | Methods and apparatus object-oriented rule-based dialogue management |
US6275817B1 (en) | 1999-07-30 | 2001-08-14 | Unisys Corporation | Semiotic decision making system used for responding to natural language queries and other purposes and components therefor |
US6351675B1 (en) | 1999-10-04 | 2002-02-26 | Medtronic, Inc. | System and method of programming an implantable medical device |
US6389406B1 (en) | 1997-07-30 | 2002-05-14 | Unisys Corporation | Semiotic decision making system for responding to natural language queries and components thereof |
US6394263B1 (en) | 1999-07-30 | 2002-05-28 | Unisys Corporation | Autognomic decision making system and method |
US6505184B1 (en) | 1999-07-30 | 2003-01-07 | Unisys Corporation | Autognomic decision making system and method |
US20030028469A1 (en) | 2001-06-29 | 2003-02-06 | International Business Machines Corporation | Methods and apparatus for enabling an electronic information marketplace |
US20030073939A1 (en) | 2001-02-23 | 2003-04-17 | Taylor Robin L. | Continuous passive motion apparatus |
US20030078838A1 (en) | 2001-10-18 | 2003-04-24 | Szmanda Jeffrey P. | Method of retrieving advertising information and use of the method |
CA2421545A1 (en) | 2002-03-13 | 2003-09-13 | Nec Corporation | Multiplex transmission system for reporting alert information to the conversion device on the receiving side without using a dedicated frame |
US20040030556A1 (en) | 1999-11-12 | 2004-02-12 | Bennett Ian M. | Speech based learning/training system using semantic decoding |
US6701516B1 (en) | 1998-05-21 | 2004-03-02 | Qifang Li | P++ software |
US6745161B1 (en) | 1999-09-17 | 2004-06-01 | Discern Communications, Inc. | System and method for incorporating concept-based retrieval within boolean search engines |
US20040117189A1 (en) | 1999-11-12 | 2004-06-17 | Bennett Ian M. | Query engine for processing voice based queries including semantic decoding |
US6782376B2 (en) | 2000-05-18 | 2004-08-24 | Hitachi, Ltd. | Reasoning method based on similarity of cases |
US20040193572A1 (en) | 2001-05-03 | 2004-09-30 | Leary Richard M. | System and method for the management, analysis, and application of data for knowledge-based organizations |
US20040249618A1 (en) | 2003-06-03 | 2004-12-09 | International Business Machines Corporation | Apparatus and method for coverage directed test |
US20050060323A1 (en) | 2003-09-17 | 2005-03-17 | Leung Ying Tat | Diagnosis of equipment failures using an integrated approach of case based reasoning and reliability analysis |
US20060112048A1 (en) | 2004-10-29 | 2006-05-25 | Talbot Patrick J | System and method for the automated discovery of unknown unknowns |
US20060122834A1 (en) | 2004-12-03 | 2006-06-08 | Bennett Ian M | Emotion detection device & method for use in distributed systems |
US20060122876A1 (en) | 2002-11-15 | 2006-06-08 | Erick Von Schweber | Method and apparatus for information surveying |
US20060256953A1 (en) | 2005-05-12 | 2006-11-16 | Knowlagent, Inc. | Method and system for improving workforce performance in a contact center |
US20070005650A1 (en) | 2005-06-30 | 2007-01-04 | The Boeing Company | Methods and systems for analyzing incident reports |
US20070061110A1 (en) | 2005-09-09 | 2007-03-15 | Canadian Space Agency | System and method for diagnosis based on set operations |
US20070094209A1 (en) | 2002-10-03 | 2007-04-26 | Steward Donald V | Cause and effect problem solving system and method |
US20070094216A1 (en) | 2005-08-02 | 2007-04-26 | Northrop Grumman Corporation | Uncertainty management in a decision-making system |
US20070288419A1 (en) | 2006-06-07 | 2007-12-13 | Motorola, Inc. | Method and apparatus for augmenting data and actions with semantic information to facilitate the autonomic operations of components and systems |
US20070288405A1 (en) | 2006-06-07 | 2007-12-13 | Motorola, Inc. | Problem solving mechanism selection facilitation apparatus and method |
US20070288467A1 (en) | 2006-06-07 | 2007-12-13 | Motorola, Inc. | Method and apparatus for harmonizing the gathering of data and issuing of commands in an autonomic computing system using model-based translation |
US20070288418A1 (en) * | 2006-06-10 | 2007-12-13 | Simon Kevin John Pope | Intelligence analysis method and system |
US7313515B2 (en) | 2006-05-01 | 2007-12-25 | Palo Alto Research Center Incorporated | Systems and methods for detecting entailment and contradiction |
US20080015891A1 (en) | 2006-07-12 | 2008-01-17 | Medai, Inc. | Method and System to Assess an Acute and Chronic Disease Impact Index |
US20080071714A1 (en) | 2006-08-21 | 2008-03-20 | Motorola, Inc. | Method and apparatus for controlling autonomic computing system processes using knowledge-based reasoning mechanisms |
US20080140657A1 (en) | 2005-02-03 | 2008-06-12 | Behnam Azvine | Document Searching Tool and Method |
US7389208B1 (en) | 2000-06-30 | 2008-06-17 | Accord Solutions, Inc. | System and method for dynamic knowledge construction |
US7421738B2 (en) | 2002-11-25 | 2008-09-02 | Honeywell International Inc. | Skeptical system |
US20080222058A1 (en) | 2006-07-10 | 2008-09-11 | Doctor Jason N | Bayesian-network-based method and system for detection of clinical-laboratory errors |
US20080270336A1 (en) | 2004-06-30 | 2008-10-30 | Northrop Grumman Corporation | System and method for the automated discovery of unknown unknowns |
CN101299303A (en) | 2000-09-01 | 2008-11-05 | 第一原则公司 | Rational inquiry method |
US7450523B1 (en) | 2005-08-01 | 2008-11-11 | Rockwell Collins, Inc. | Control of reconfigurable SIS/MAC protocols used in wireless communication devices |
US20080294415A1 (en) | 2007-05-24 | 2008-11-27 | Palo Alto Research Center Incorporated | Troubleshooting temporal behavior in "combinational" circuits |
US20080294578A1 (en) | 2007-05-24 | 2008-11-27 | Palo Alto Research Center Incorporated | Diagnosing intermittent faults |
US20080306899A1 (en) | 2007-06-07 | 2008-12-11 | Gregory Michelle L | Methods, apparatus, and computer-readable media for analyzing conversational-type data |
US20090006320A1 (en) | 2007-04-01 | 2009-01-01 | Nec Laboratories America, Inc. | Runtime Semantic Query Optimization for Event Stream Processing |
US20090018802A1 (en) | 2007-07-10 | 2009-01-15 | Palo Alto Research Center Incorporated | Modeling when connections are the problem |
US20090070311A1 (en) | 2007-09-07 | 2009-03-12 | At&T Corp. | System and method using a discriminative learning approach for question answering |
US20090165110A1 (en) | 2007-12-21 | 2009-06-25 | Microsoft Corporation | Delegation in logic-based access control |
US20090164469A1 (en) | 2007-12-21 | 2009-06-25 | Microsoft Corporation | Abducing assertion to support access query |
US20090193493A1 (en) | 2008-01-28 | 2009-07-30 | Microsoft Corporation | Access policy analysis |
US20090222921A1 (en) | 2008-02-29 | 2009-09-03 | Utah State University | Technique and Architecture for Cognitive Coordination of Resources in a Distributed Network |
US20090327172A1 (en) | 2008-06-27 | 2009-12-31 | Motorola, Inc. | Adaptive knowledge-based reasoning in autonomic computing systems |
US20090328133A1 (en) | 2008-06-27 | 2009-12-31 | Motorola, Inc. | Capability management for network elements |
US7647225B2 (en) | 1999-11-12 | 2010-01-12 | Phoenix Solutions, Inc. | Adjustable resource based speech recognition system |
US20100010872A1 (en) | 2008-07-07 | 2010-01-14 | Glen Drummond | Persona-based customer relationship management tools and methods for sales support |
US20100083056A1 (en) | 2008-09-26 | 2010-04-01 | Bae Systems Information And Electronic Systems Integration Inc. | Prognostic diagnostic capability tracking system |
US7694226B2 (en) | 2006-01-03 | 2010-04-06 | Eastman Kodak Company | System and method for generating a work of communication with supplemental context |
US7698131B2 (en) | 1999-11-12 | 2010-04-13 | Phoenix Solutions, Inc. | Speech recognition system for client devices having differing computing capabilities |
US20100205649A1 (en) | 2009-02-06 | 2010-08-12 | Microsoft Corporation | Credential gathering with deferred instantiation |
CN101872345A (en) | 2009-04-27 | 2010-10-27 | 刘冬梅 | Logical reasoning mechanism being suitable for legal expert system |
US20110093463A1 (en) | 2009-10-21 | 2011-04-21 | Nokia Corporation | Method and system for projecting and injecting information spaces |
US20110106745A1 (en) | 2009-11-03 | 2011-05-05 | Tatu Ylonen Oy Ltd | Semantic Network with Selective Indexing |
US20110118905A1 (en) | 2009-11-16 | 2011-05-19 | Honeywell International Inc. | Methods systems and apparatus for analyzing complex systems via prognostic reasoning |
US20110153362A1 (en) | 2009-12-17 | 2011-06-23 | Valin David A | Method and mechanism for identifying protecting, requesting, assisting and managing information |
US20110218855A1 (en) | 2010-03-03 | 2011-09-08 | Platformation, Inc. | Offering Promotions Based on Query Analysis |
US20110231356A1 (en) | 2009-07-01 | 2011-09-22 | Quantum Leap Research, Inc. | FlexSCAPE: Data Driven Hypothesis Testing and Generation System |
US8060567B2 (en) | 2006-04-12 | 2011-11-15 | Google Inc. | Method, system, graphical user interface, and data structure for creating electronic calendar entries from email messages |
US20110314075A1 (en) | 2010-06-18 | 2011-12-22 | Nokia Corporation | Method and apparatus for managing distributed computations within a computation space |
US20120072386A1 (en) | 2010-09-17 | 2012-03-22 | Fluor Technologies Corporation | Intelligent Plant Development Library Environment |
US20120101793A1 (en) | 2010-10-22 | 2012-04-26 | Airbus Operations (S.A.S.) | Method, devices and computer program for assisting in the diagnostic of an aircraft system, using failure condition graphs |
US20120143013A1 (en) | 2010-11-03 | 2012-06-07 | Davis Iii Robert Thompson | Proactive Patient Health Care Inference Engines and Systems |
US20120158391A1 (en) | 2010-04-29 | 2012-06-21 | The Regents of the University of California, a U.S. entity | Pathway recognition algorithm using data integration on genomic models (PAGADIGM) |
US20120185424A1 (en) | 2009-07-01 | 2012-07-19 | Quantum Leap Research, Inc. | FlexSCAPE: Data Driven Hypothesis Testing and Generation System |
US20120185330A1 (en) | 2011-01-14 | 2012-07-19 | Platformation, Inc. | Discovery and Publishing Among Multiple Sellers and Multiple Buyers |
US20120197751A1 (en) | 2011-01-27 | 2012-08-02 | Electronic Entertainment Design And Research | Product recommendations and weighting optimization systems |
US20120197874A1 (en) | 2011-01-27 | 2012-08-02 | Electronic Entertainment Design And Research | Game recommendation engine for mapping games to disabilities |
US20120197653A1 (en) | 2011-01-27 | 2012-08-02 | Electronic Entertainment Design And Research | Brand identification, systems and methods |
US20120226650A1 (en) | 2011-03-03 | 2012-09-06 | Nicholas Witchey | Black Box Innovation, Systems and Methods |
US20120301864A1 (en) | 2011-05-26 | 2012-11-29 | International Business Machines Corporation | User interface for an evidence-based, hypothesis-generating decision support system |
US20130054506A1 (en) | 2010-04-23 | 2013-02-28 | Siemens Aktiengesellschaft | Method and device for controlling an industrial system |
US20130110573A1 (en) | 2011-11-02 | 2013-05-02 | Fluor Technologies Corporation | Identification and optimization of over-engineered components |
US8463556B2 (en) | 2007-08-30 | 2013-06-11 | Exelis Inc. | System and method for radioisotope identification |
US20130203038A1 (en) | 2011-08-10 | 2013-08-08 | Learningmate Solutions Private Limited | System, method and apparatus for managing education and training workflows |
US20130204813A1 (en) | 2012-01-20 | 2013-08-08 | Fluential, Llc | Self-learning, context aware virtual assistants, systems and methods |
US20130211841A1 (en) | 2012-02-15 | 2013-08-15 | Fluential, Llc | Multi-Dimensional Interactions and Recall |
US20130212130A1 (en) | 2012-02-15 | 2013-08-15 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US8572290B1 (en) | 2011-05-02 | 2013-10-29 | Board Of Supervisors Of Louisiana State University And Agricultural And Mechanical College | System and architecture for robust management of resources in a wide-area network |
US20130310653A1 (en) | 2012-05-16 | 2013-11-21 | Sonja Zillner | Method and system for supporting a clinical diagnosis |
US8660849B2 (en) | 2010-01-18 | 2014-02-25 | Apple Inc. | Prioritizing selection criteria by automated assistant |
US8670985B2 (en) | 2010-01-13 | 2014-03-11 | Apple Inc. | Devices and methods for identifying a prompt corresponding to a voice input in a sequence of prompts |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US8682649B2 (en) | 2009-11-12 | 2014-03-25 | Apple Inc. | Sentiment prediction from textual data |
US8688446B2 (en) | 2008-02-22 | 2014-04-01 | Apple Inc. | Providing text input using speech data and non-speech data |
US8706472B2 (en) | 2011-08-11 | 2014-04-22 | Apple Inc. | Method for disambiguating multiple readings in language conversion |
US8712776B2 (en) | 2008-09-29 | 2014-04-29 | Apple Inc. | Systems and methods for selective text to speech synthesis |
US8713021B2 (en) | 2010-07-07 | 2014-04-29 | Apple Inc. | Unsupervised document clustering using latent semantic density analysis |
US8719006B2 (en) | 2010-08-27 | 2014-05-06 | Apple Inc. | Combined statistical and rule-based part-of-speech tagging for text-to-speech synthesis |
US8718047B2 (en) | 2001-10-22 | 2014-05-06 | Apple Inc. | Text to speech conversion of text messages from mobile communication devices |
US8719014B2 (en) | 2010-09-27 | 2014-05-06 | Apple Inc. | Electronic device with text error correction based on voice recognition data |
US8719005B1 (en) | 2006-02-10 | 2014-05-06 | Rusty Shawn Lee | Method and apparatus for using directed reasoning to respond to natural language queries |
US20140129504A1 (en) | 2011-03-22 | 2014-05-08 | Patrick Soon-Shiong | Reasoning Engines |
US20140149337A1 (en) | 2011-07-12 | 2014-05-29 | Siemens Aktiengesellschaft | Actuation of a technical system |
US8743708B1 (en) | 2005-08-01 | 2014-06-03 | Rockwell Collins, Inc. | Device and method supporting cognitive, dynamic media access control |
US8751238B2 (en) | 2009-03-09 | 2014-06-10 | Apple Inc. | Systems and methods for determining the language to use for speech generated by a text to speech engine |
US8762156B2 (en) | 2011-09-28 | 2014-06-24 | Apple Inc. | Speech recognition repair using contextual information |
US8768869B1 (en) | 2012-04-19 | 2014-07-01 | The United States Of America As Represented By The Secretary Of The Navy | BRIAN: a basic regimen for intelligent analysis using networks |
US8768702B2 (en) | 2008-09-05 | 2014-07-01 | Apple Inc. | Multi-tiered voice feedback in an electronic device |
US8775442B2 (en) | 2012-05-15 | 2014-07-08 | Apple Inc. | Semantic search using a single-source semantic model |
US20140195664A1 (en) | 2012-02-15 | 2014-07-10 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US8781836B2 (en) | 2011-02-22 | 2014-07-15 | Apple Inc. | Hearing assistance system for providing consistent human speech |
US20140223561A1 (en) | 2013-02-05 | 2014-08-07 | Hackproof Technologies Inc. | Domain-specific Hardwired Symbolic Machine |
US8812294B2 (en) | 2011-06-21 | 2014-08-19 | Apple Inc. | Translating phrases from one language into another using an order-based set of declarative rules |
US20140249875A1 (en) | 2011-12-21 | 2014-09-04 | International Business Machines Corporation | Detecting cases with conflicting rules |
US20140277755A1 (en) | 2011-10-28 | 2014-09-18 | Siemens Aktiengesellschaft | Control of a machine |
US20140270467A1 (en) | 2013-03-18 | 2014-09-18 | Kenneth Gerald Blemel | System for Anti-Tamper Parcel Packaging, Shipment, Receipt, and Storage |
US8862252B2 (en) | 2009-01-30 | 2014-10-14 | Apple Inc. | Audio user interface for displayless electronic device |
US8898568B2 (en) | 2008-09-09 | 2014-11-25 | Apple Inc. | Audio user interface |
US20140358865A1 (en) | 2011-12-28 | 2014-12-04 | Hans-Gerd Brummel | Processing a technical system |
US8935167B2 (en) | 2012-09-25 | 2015-01-13 | Apple Inc. | Exemplar-based latent perceptual modeling for automatic speech recognition |
US20150039648A1 (en) | 2012-04-12 | 2015-02-05 | Tata Consultancy Services Limited | System and a method for reasoning and running continuous queries over data streams |
US20150046388A1 (en) | 2013-08-07 | 2015-02-12 | Wright State University | Semantic perception |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
US20150079556A1 (en) | 2013-09-13 | 2015-03-19 | Maarit LAITINEN | Teaching means for mathematics |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US20150096026A1 (en) | 2013-03-15 | 2015-04-02 | Cyberricade, Inc. | Cyber security |
US20150154178A1 (en) | 2013-04-19 | 2015-06-04 | Empire Technology Development Llc | Coarse semantic data set enhancement for a reasoning task |
US9053089B2 (en) | 2007-10-02 | 2015-06-09 | Apple Inc. | Part-of-speech tagging using latent analogy |
US20150199607A1 (en) | 2013-05-31 | 2015-07-16 | Empire Technology Development Llc | Incremental reasoning based on scalable and dynamical semantic data |
US20150254330A1 (en) | 2013-04-11 | 2015-09-10 | Oracle International Corporation | Knowledge-intensive data processing system |
US20150261825A1 (en) | 2012-07-03 | 2015-09-17 | Siemens Aktiengesellschaft | Determination of the Suitability of a Resource |
US20150269639A1 (en) | 2014-03-03 | 2015-09-24 | Avi Mistriel | Referent-Centric Social Networking |
US9160738B2 (en) | 2010-05-27 | 2015-10-13 | Microsoft Corporation | Delegation-based authorization |
US20150310497A1 (en) | 2009-12-17 | 2015-10-29 | David Valin | Method and process for registration, creation and management of micro shares of real or intangible properties and advertisements in a network system |
US9213821B2 (en) | 2010-02-24 | 2015-12-15 | Infosys Limited | System and method for monitoring human interaction |
US9258306B2 (en) | 2012-05-11 | 2016-02-09 | Infosys Limited | Methods for confirming user interaction in response to a request for a computer provided service and devices thereof |
US9256713B2 (en) | 2007-08-30 | 2016-02-09 | Exelis Inc. | Library generation for detection and identification of shielded radioisotopes |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
-
2011
- 2011-07-12 DE DE102011079034A patent/DE102011079034A1/en not_active Ceased
-
2012
- 2012-07-02 CN CN201280044282.4A patent/CN103782245B/en active Active
- 2012-07-02 EP EP12743402.5A patent/EP2712429A1/en not_active Ceased
- 2012-07-02 WO PCT/EP2012/062815 patent/WO2013007547A1/en active Application Filing
- 2012-07-02 US US14/232,315 patent/US9449275B2/en active Active
Patent Citations (251)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4783741A (en) | 1983-08-08 | 1988-11-08 | Bernhard Mitterauer | Computer system for simulating reticular formation operation |
US4813013A (en) | 1984-03-01 | 1989-03-14 | The Cadware Group, Ltd. | Schematic diagram generating system using library of general purpose interactively selectable graphic primitives to create special applications icons |
US4656603A (en) | 1984-03-01 | 1987-04-07 | The Cadware Group, Ltd. | Schematic diagram generating system using library of general purpose interactively selectable graphic primitives to create special applications icons |
US5852811A (en) | 1987-04-15 | 1998-12-22 | Proprietary Financial Products, Inc. | Method for managing financial accounts by a preferred allocation of funds among accounts |
US5018075A (en) | 1989-03-24 | 1991-05-21 | Bull Hn Information Systems Inc. | Unknown response processing in a diagnostic expert system |
US5293323A (en) | 1991-10-24 | 1994-03-08 | General Electric Company | Method for fault diagnosis by assessment of confidence measure |
US5870701A (en) | 1992-08-21 | 1999-02-09 | Canon Kabushiki Kaisha | Control signal processing method and apparatus having natural language interfacing capabilities |
US5631831A (en) | 1993-02-26 | 1997-05-20 | Spx Corporation | Diagnosis method for vehicle systems |
CN1092151A (en) | 1993-03-08 | 1994-09-14 | 三洋电机株式会社 | The control method of air conditioner |
US5812994A (en) | 1993-05-20 | 1998-09-22 | Canon Kabushiki Kaisha | Apparatus and method for data processing and/or for control |
US5712960A (en) | 1993-07-02 | 1998-01-27 | Cv Soft, S.R.L. | System and methods for intelligent database management using abductive reasoning |
US5802256A (en) | 1994-05-09 | 1998-09-01 | Microsoft Corporation | Generating improved belief networks |
US6529888B1 (en) | 1994-05-09 | 2003-03-04 | Microsoft Corporation | Generating improved belief networks |
US5810747A (en) | 1996-08-21 | 1998-09-22 | Interactive Remote Site Technology, Inc. | Remote site medical intervention system |
US6012152A (en) | 1996-11-27 | 2000-01-04 | Telefonaktiebolaget Lm Ericsson (Publ) | Software fault management system |
US5884294A (en) | 1997-04-18 | 1999-03-16 | Northrop Grumman Corporation | System and method for functional recognition of emitters |
US6389406B1 (en) | 1997-07-30 | 2002-05-14 | Unisys Corporation | Semiotic decision making system for responding to natural language queries and components thereof |
US6044347A (en) | 1997-08-05 | 2000-03-28 | Lucent Technologies Inc. | Methods and apparatus object-oriented rule-based dialogue management |
US6701516B1 (en) | 1998-05-21 | 2004-03-02 | Qifang Li | P++ software |
US6275817B1 (en) | 1999-07-30 | 2001-08-14 | Unisys Corporation | Semiotic decision making system used for responding to natural language queries and other purposes and components therefor |
US6394263B1 (en) | 1999-07-30 | 2002-05-28 | Unisys Corporation | Autognomic decision making system and method |
US6505184B1 (en) | 1999-07-30 | 2003-01-07 | Unisys Corporation | Autognomic decision making system and method |
US6278987B1 (en) | 1999-07-30 | 2001-08-21 | Unisys Corporation | Data processing method for a semiotic decision making system used for responding to natural language queries and other purposes |
US6910003B1 (en) | 1999-09-17 | 2005-06-21 | Discern Communications, Inc. | System, method and article of manufacture for concept based information searching |
US6745161B1 (en) | 1999-09-17 | 2004-06-01 | Discern Communications, Inc. | System and method for incorporating concept-based retrieval within boolean search engines |
US6351675B1 (en) | 1999-10-04 | 2002-02-26 | Medtronic, Inc. | System and method of programming an implantable medical device |
US7702508B2 (en) | 1999-11-12 | 2010-04-20 | Phoenix Solutions, Inc. | System and method for natural language processing of query answers |
US20100005081A1 (en) | 1999-11-12 | 2010-01-07 | Bennett Ian M | Systems for natural language processing of sentence based queries |
US7725321B2 (en) | 1999-11-12 | 2010-05-25 | Phoenix Solutions, Inc. | Speech based query system using semantic decoding |
US7725307B2 (en) | 1999-11-12 | 2010-05-25 | Phoenix Solutions, Inc. | Query engine for processing voice based queries including semantic decoding |
US20040117189A1 (en) | 1999-11-12 | 2004-06-17 | Bennett Ian M. | Query engine for processing voice based queries including semantic decoding |
US8352277B2 (en) | 1999-11-12 | 2013-01-08 | Phoenix Solutions, Inc. | Method of interacting through speech with a web-connected server |
US20080255845A1 (en) | 1999-11-12 | 2008-10-16 | Bennett Ian M | Speech Based Query System Using Semantic Decoding |
US20040236580A1 (en) | 1999-11-12 | 2004-11-25 | Bennett Ian M. | Method for processing speech using dynamic grammars |
US20040249635A1 (en) | 1999-11-12 | 2004-12-09 | Bennett Ian M. | Method for processing speech signal features for streaming transport |
US7725320B2 (en) | 1999-11-12 | 2010-05-25 | Phoenix Solutions, Inc. | Internet based speech recognition system with dynamic grammars |
US20120265531A1 (en) | 1999-11-12 | 2012-10-18 | Bennett Ian M | Speech based learning/training system using semantic decoding |
US20050080614A1 (en) | 1999-11-12 | 2005-04-14 | Bennett Ian M. | System & method for natural language processing of query answers |
US20050086049A1 (en) | 1999-11-12 | 2005-04-21 | Bennett Ian M. | System & method for processing sentence based queries |
US20050086046A1 (en) | 1999-11-12 | 2005-04-21 | Bennett Ian M. | System & method for natural language processing of sentence based queries |
US20050086059A1 (en) | 1999-11-12 | 2005-04-21 | Bennett Ian M. | Partial speech processing device & method for use in distributed systems |
US20080215327A1 (en) | 1999-11-12 | 2008-09-04 | Bennett Ian M | Method For Processing Speech Data For A Distributed Recognition System |
US7698131B2 (en) | 1999-11-12 | 2010-04-13 | Phoenix Solutions, Inc. | Speech recognition system for client devices having differing computing capabilities |
US7729904B2 (en) | 1999-11-12 | 2010-06-01 | Phoenix Solutions, Inc. | Partial speech processing device and method for use in distributed systems |
US7672841B2 (en) | 1999-11-12 | 2010-03-02 | Phoenix Solutions, Inc. | Method for processing speech data for a distributed recognition system |
US20060235696A1 (en) | 1999-11-12 | 2006-10-19 | Bennett Ian M | Network based interactive speech recognition system |
US7657424B2 (en) | 1999-11-12 | 2010-02-02 | Phoenix Solutions, Inc. | System and method for processing sentence based queries |
US20100228540A1 (en) | 1999-11-12 | 2010-09-09 | Phoenix Solutions, Inc. | Methods and Systems for Query-Based Searching Using Spoken Input |
US7647225B2 (en) | 1999-11-12 | 2010-01-12 | Phoenix Solutions, Inc. | Adjustable resource based speech recognition system |
US20040030556A1 (en) | 1999-11-12 | 2004-02-12 | Bennett Ian M. | Speech based learning/training system using semantic decoding |
US20100235341A1 (en) | 1999-11-12 | 2010-09-16 | Phoenix Solutions, Inc. | Methods and Systems for Searching Using Spoken Input and User Context Information |
US7831426B2 (en) | 1999-11-12 | 2010-11-09 | Phoenix Solutions, Inc. | Network based interactive speech recognition system |
US9190063B2 (en) | 1999-11-12 | 2015-11-17 | Nuance Communications, Inc. | Multi-language speech recognition system |
US7624007B2 (en) | 1999-11-12 | 2009-11-24 | Phoenix Solutions, Inc. | System and method for natural language processing of sentence based queries |
US9076448B2 (en) | 1999-11-12 | 2015-07-07 | Nuance Communications, Inc. | Distributed real time speech recognition system |
US8762152B2 (en) | 1999-11-12 | 2014-06-24 | Nuance Communications, Inc. | Speech recognition system interactive agent |
US7873519B2 (en) | 1999-11-12 | 2011-01-18 | Phoenix Solutions, Inc. | Natural language speech lattice containing semantic variants |
US20080300878A1 (en) | 1999-11-12 | 2008-12-04 | Bennett Ian M | Method For Transporting Speech Data For A Distributed Recognition System |
US7555431B2 (en) | 1999-11-12 | 2009-06-30 | Phoenix Solutions, Inc. | Method for processing speech using dynamic grammars |
US7912702B2 (en) | 1999-11-12 | 2011-03-22 | Phoenix Solutions, Inc. | Statistical language model trained with semantic variants |
US20080052078A1 (en) | 1999-11-12 | 2008-02-28 | Bennett Ian M | Statistical Language Model Trained With Semantic Variants |
US20080059153A1 (en) | 1999-11-12 | 2008-03-06 | Bennett Ian M | Natural Language Speech Lattice Containing Semantic Variants |
US20090157401A1 (en) | 1999-11-12 | 2009-06-18 | Bennett Ian M | Semantic Decoding of User Queries |
US7392185B2 (en) | 1999-11-12 | 2008-06-24 | Phoenix Solutions, Inc. | Speech based learning/training system using semantic decoding |
US7376556B2 (en) | 1999-11-12 | 2008-05-20 | Phoenix Solutions, Inc. | Method for processing speech signal features for streaming transport |
US8229734B2 (en) | 1999-11-12 | 2012-07-24 | Phoenix Solutions, Inc. | Semantic decoding of user queries |
US6782376B2 (en) | 2000-05-18 | 2004-08-24 | Hitachi, Ltd. | Reasoning method based on similarity of cases |
US7389208B1 (en) | 2000-06-30 | 2008-06-17 | Accord Solutions, Inc. | System and method for dynamic knowledge construction |
US20090018984A1 (en) | 2000-06-30 | 2009-01-15 | Solinsky James C | System and method for dynamic knowledge construction |
CN101299303A (en) | 2000-09-01 | 2008-11-05 | 第一原则公司 | Rational inquiry method |
US20030073939A1 (en) | 2001-02-23 | 2003-04-17 | Taylor Robin L. | Continuous passive motion apparatus |
US20040193572A1 (en) | 2001-05-03 | 2004-09-30 | Leary Richard M. | System and method for the management, analysis, and application of data for knowledge-based organizations |
US20030028469A1 (en) | 2001-06-29 | 2003-02-06 | International Business Machines Corporation | Methods and apparatus for enabling an electronic information marketplace |
US20030078838A1 (en) | 2001-10-18 | 2003-04-24 | Szmanda Jeffrey P. | Method of retrieving advertising information and use of the method |
US20080109318A1 (en) | 2001-10-18 | 2008-05-08 | Szmanda Jeffrey P | Business Method For Facilitating Advertisement Response |
US8718047B2 (en) | 2001-10-22 | 2014-05-06 | Apple Inc. | Text to speech conversion of text messages from mobile communication devices |
CA2421545A1 (en) | 2002-03-13 | 2003-09-13 | Nec Corporation | Multiplex transmission system for reporting alert information to the conversion device on the receiving side without using a dedicated frame |
US20070094209A1 (en) | 2002-10-03 | 2007-04-26 | Steward Donald V | Cause and effect problem solving system and method |
US20060122876A1 (en) | 2002-11-15 | 2006-06-08 | Erick Von Schweber | Method and apparatus for information surveying |
US20120203727A1 (en) | 2002-11-15 | 2012-08-09 | Erick Von Schweber | Method and apparatus for information surveying |
US8170906B2 (en) | 2002-11-15 | 2012-05-01 | Erick Von Schweber | Method and apparatus for information surveying |
US7421738B2 (en) | 2002-11-25 | 2008-09-02 | Honeywell International Inc. | Skeptical system |
US7181376B2 (en) | 2003-06-03 | 2007-02-20 | International Business Machines Corporation | Apparatus and method for coverage directed test |
US20040249618A1 (en) | 2003-06-03 | 2004-12-09 | International Business Machines Corporation | Apparatus and method for coverage directed test |
US7730020B2 (en) | 2003-09-17 | 2010-06-01 | International Business Machines Corporation | Diagnosis of equipment failures using an integrated approach of case based reasoning and reliability analysis |
US20050060323A1 (en) | 2003-09-17 | 2005-03-17 | Leung Ying Tat | Diagnosis of equipment failures using an integrated approach of case based reasoning and reliability analysis |
US20070288795A1 (en) | 2003-09-17 | 2007-12-13 | Leung Ying T | Diagnosis of equipment failures using an integrated approach of case based reasoning and reliability analysis |
US7313573B2 (en) | 2003-09-17 | 2007-12-25 | International Business Machines Corporation | Diagnosis of equipment failures using an integrated approach of case based reasoning and reliability analysis |
US8078559B2 (en) | 2004-06-30 | 2011-12-13 | Northrop Grumman Systems Corporation | System and method for the automated discovery of unknown unknowns |
US20080270336A1 (en) | 2004-06-30 | 2008-10-30 | Northrop Grumman Corporation | System and method for the automated discovery of unknown unknowns |
US20060112048A1 (en) | 2004-10-29 | 2006-05-25 | Talbot Patrick J | System and method for the automated discovery of unknown unknowns |
US20060122834A1 (en) | 2004-12-03 | 2006-06-08 | Bennett Ian M | Emotion detection device & method for use in distributed systems |
US7836077B2 (en) | 2005-02-03 | 2010-11-16 | British Telecommunications Plc | Document searching tool and method |
US20080140657A1 (en) | 2005-02-03 | 2008-06-12 | Behnam Azvine | Document Searching Tool and Method |
US20060256953A1 (en) | 2005-05-12 | 2006-11-16 | Knowlagent, Inc. | Method and system for improving workforce performance in a contact center |
US20070005650A1 (en) | 2005-06-30 | 2007-01-04 | The Boeing Company | Methods and systems for analyzing incident reports |
US7613667B2 (en) | 2005-06-30 | 2009-11-03 | The Boeing Company | Methods and systems for analyzing incident reports |
US8743708B1 (en) | 2005-08-01 | 2014-06-03 | Rockwell Collins, Inc. | Device and method supporting cognitive, dynamic media access control |
US7450523B1 (en) | 2005-08-01 | 2008-11-11 | Rockwell Collins, Inc. | Control of reconfigurable SIS/MAC protocols used in wireless communication devices |
US7606784B2 (en) | 2005-08-02 | 2009-10-20 | Northrop Grumman Corporation | Uncertainty management in a decision-making system |
US20070094216A1 (en) | 2005-08-02 | 2007-04-26 | Northrop Grumman Corporation | Uncertainty management in a decision-making system |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US20070061110A1 (en) | 2005-09-09 | 2007-03-15 | Canadian Space Agency | System and method for diagnosis based on set operations |
US7975227B2 (en) | 2006-01-03 | 2011-07-05 | Eastman Kodak Company | System and method for generating a work of communication with supplemental context |
US7694226B2 (en) | 2006-01-03 | 2010-04-06 | Eastman Kodak Company | System and method for generating a work of communication with supplemental context |
US8375303B2 (en) | 2006-01-03 | 2013-02-12 | Eastman Kodak Company | System and method for generating a work of communication with supplemental context |
US8719005B1 (en) | 2006-02-10 | 2014-05-06 | Rusty Shawn Lee | Method and apparatus for using directed reasoning to respond to natural language queries |
US8060567B2 (en) | 2006-04-12 | 2011-11-15 | Google Inc. | Method, system, graphical user interface, and data structure for creating electronic calendar entries from email messages |
US8244821B2 (en) | 2006-04-12 | 2012-08-14 | Google Inc. | Method, system, graphical user interface, and data structure for creating electronic calendar entries from email messages |
US8375099B2 (en) | 2006-04-12 | 2013-02-12 | Google Inc. | Method, system, graphical user interface, and data structure for creating electronic calendar entries from email messages |
US9092109B2 (en) | 2006-04-12 | 2015-07-28 | Google Inc. | Method, system, graphical user interface, and data structure for creating electronic calendar entries from email messages |
US7313515B2 (en) | 2006-05-01 | 2007-12-25 | Palo Alto Research Center Incorporated | Systems and methods for detecting entailment and contradiction |
US20070288405A1 (en) | 2006-06-07 | 2007-12-13 | Motorola, Inc. | Problem solving mechanism selection facilitation apparatus and method |
US20070288467A1 (en) | 2006-06-07 | 2007-12-13 | Motorola, Inc. | Method and apparatus for harmonizing the gathering of data and issuing of commands in an autonomic computing system using model-based translation |
US20070288419A1 (en) | 2006-06-07 | 2007-12-13 | Motorola, Inc. | Method and apparatus for augmenting data and actions with semantic information to facilitate the autonomic operations of components and systems |
US7720787B2 (en) | 2006-06-10 | 2010-05-18 | Distip Pty Limited | Intelligence analysis method and system using subjective logic |
US20070288418A1 (en) * | 2006-06-10 | 2007-12-13 | Simon Kevin John Pope | Intelligence analysis method and system |
US7783582B2 (en) | 2006-07-10 | 2010-08-24 | University Of Washington | Bayesian-network-based method and system for detection of clinical-laboratory errors using synthetic errors |
US20080222058A1 (en) | 2006-07-10 | 2008-09-11 | Doctor Jason N | Bayesian-network-based method and system for detection of clinical-laboratory errors |
US20080015891A1 (en) | 2006-07-12 | 2008-01-17 | Medai, Inc. | Method and System to Assess an Acute and Chronic Disease Impact Index |
US20080071714A1 (en) | 2006-08-21 | 2008-03-20 | Motorola, Inc. | Method and apparatus for controlling autonomic computing system processes using knowledge-based reasoning mechanisms |
US9117447B2 (en) | 2006-09-08 | 2015-08-25 | Apple Inc. | Using event alert text as input to an automated assistant |
US8942986B2 (en) | 2006-09-08 | 2015-01-27 | Apple Inc. | Determining user intent based on ontologies of domains |
US8930191B2 (en) | 2006-09-08 | 2015-01-06 | Apple Inc. | Paraphrasing of user requests and results by automated digital assistant |
US20090006320A1 (en) | 2007-04-01 | 2009-01-01 | Nec Laboratories America, Inc. | Runtime Semantic Query Optimization for Event Stream Processing |
US8065319B2 (en) | 2007-04-01 | 2011-11-22 | Nec Laboratories America, Inc. | Runtime semantic query optimization for event stream processing |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
US8024610B2 (en) | 2007-05-24 | 2011-09-20 | Palo Alto Research Center Incorporated | Diagnosing intermittent faults |
US8271257B2 (en) | 2007-05-24 | 2012-09-18 | Palo Alto Research Center Incorporated | Troubleshooting temporal behavior in “combinational” circuits |
US20080294578A1 (en) | 2007-05-24 | 2008-11-27 | Palo Alto Research Center Incorporated | Diagnosing intermittent faults |
US20080294415A1 (en) | 2007-05-24 | 2008-11-27 | Palo Alto Research Center Incorporated | Troubleshooting temporal behavior in "combinational" circuits |
US20080306899A1 (en) | 2007-06-07 | 2008-12-11 | Gregory Michelle L | Methods, apparatus, and computer-readable media for analyzing conversational-type data |
US7962321B2 (en) | 2007-07-10 | 2011-06-14 | Palo Alto Research Center Incorporated | Modeling when connections are the problem |
US20090018802A1 (en) | 2007-07-10 | 2009-01-15 | Palo Alto Research Center Incorporated | Modeling when connections are the problem |
US9256713B2 (en) | 2007-08-30 | 2016-02-09 | Exelis Inc. | Library generation for detection and identification of shielded radioisotopes |
US8463556B2 (en) | 2007-08-30 | 2013-06-11 | Exelis Inc. | System and method for radioisotope identification |
US20090070311A1 (en) | 2007-09-07 | 2009-03-12 | At&T Corp. | System and method using a discriminative learning approach for question answering |
US8543565B2 (en) | 2007-09-07 | 2013-09-24 | At&T Intellectual Property Ii, L.P. | System and method using a discriminative learning approach for question answering |
US9053089B2 (en) | 2007-10-02 | 2015-06-09 | Apple Inc. | Part-of-speech tagging using latent analogy |
US20090165110A1 (en) | 2007-12-21 | 2009-06-25 | Microsoft Corporation | Delegation in logic-based access control |
US8607311B2 (en) | 2007-12-21 | 2013-12-10 | Microsoft Corporation | Delegation in logic-based access control |
US20090164469A1 (en) | 2007-12-21 | 2009-06-25 | Microsoft Corporation | Abducing assertion to support access query |
US8010560B2 (en) | 2007-12-21 | 2011-08-30 | Microsoft Corporation | Abducing assertion to support access query |
US8839344B2 (en) | 2008-01-28 | 2014-09-16 | Microsoft Corporation | Access policy analysis |
US20090193493A1 (en) | 2008-01-28 | 2009-07-30 | Microsoft Corporation | Access policy analysis |
US8688446B2 (en) | 2008-02-22 | 2014-04-01 | Apple Inc. | Providing text input using speech data and non-speech data |
US20090222921A1 (en) | 2008-02-29 | 2009-09-03 | Utah State University | Technique and Architecture for Cognitive Coordination of Resources in a Distributed Network |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US20090328133A1 (en) | 2008-06-27 | 2009-12-31 | Motorola, Inc. | Capability management for network elements |
US20090327172A1 (en) | 2008-06-27 | 2009-12-31 | Motorola, Inc. | Adaptive knowledge-based reasoning in autonomic computing systems |
US20100010872A1 (en) | 2008-07-07 | 2010-01-14 | Glen Drummond | Persona-based customer relationship management tools and methods for sales support |
US8768702B2 (en) | 2008-09-05 | 2014-07-01 | Apple Inc. | Multi-tiered voice feedback in an electronic device |
US8898568B2 (en) | 2008-09-09 | 2014-11-25 | Apple Inc. | Audio user interface |
US20100083056A1 (en) | 2008-09-26 | 2010-04-01 | Bae Systems Information And Electronic Systems Integration Inc. | Prognostic diagnostic capability tracking system |
US8712776B2 (en) | 2008-09-29 | 2014-04-29 | Apple Inc. | Systems and methods for selective text to speech synthesis |
US8713119B2 (en) | 2008-10-02 | 2014-04-29 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US8762469B2 (en) | 2008-10-02 | 2014-06-24 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US8676904B2 (en) | 2008-10-02 | 2014-03-18 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US8862252B2 (en) | 2009-01-30 | 2014-10-14 | Apple Inc. | Audio user interface for displayless electronic device |
US20100205649A1 (en) | 2009-02-06 | 2010-08-12 | Microsoft Corporation | Credential gathering with deferred instantiation |
US8751238B2 (en) | 2009-03-09 | 2014-06-10 | Apple Inc. | Systems and methods for determining the language to use for speech generated by a text to speech engine |
CN101872345A (en) | 2009-04-27 | 2010-10-27 | 刘冬梅 | Logical reasoning mechanism being suitable for legal expert system |
US20120185424A1 (en) | 2009-07-01 | 2012-07-19 | Quantum Leap Research, Inc. | FlexSCAPE: Data Driven Hypothesis Testing and Generation System |
US20110231356A1 (en) | 2009-07-01 | 2011-09-22 | Quantum Leap Research, Inc. | FlexSCAPE: Data Driven Hypothesis Testing and Generation System |
US8458229B2 (en) | 2009-10-21 | 2013-06-04 | Nokia Corporation | Method and system for projecting and injecting information spaces |
US20110093463A1 (en) | 2009-10-21 | 2011-04-21 | Nokia Corporation | Method and system for projecting and injecting information spaces |
US20110289045A1 (en) | 2009-11-03 | 2011-11-24 | Tatu Ylonen Oy Ltd | Inference over semantic network with some links omitted from indexes |
US8285664B2 (en) | 2009-11-03 | 2012-10-09 | Clausal Computing Oy | Semantic network with selective indexing |
US8660974B2 (en) | 2009-11-03 | 2014-02-25 | Clausal Computing Oy | Inference over semantic network with some links omitted from indexes |
US8666923B2 (en) | 2009-11-03 | 2014-03-04 | Clausal Computing Oy | Semantic network clustering influenced by index omissions |
US20110106745A1 (en) | 2009-11-03 | 2011-05-05 | Tatu Ylonen Oy Ltd | Semantic Network with Selective Indexing |
US20110289039A1 (en) | 2009-11-03 | 2011-11-24 | Tatu Ylonen Oy Ltd | Semantic network clustering influenced by index omissions |
US8682649B2 (en) | 2009-11-12 | 2014-03-25 | Apple Inc. | Sentiment prediction from textual data |
US20110118905A1 (en) | 2009-11-16 | 2011-05-19 | Honeywell International Inc. | Methods systems and apparatus for analyzing complex systems via prognostic reasoning |
US8285438B2 (en) | 2009-11-16 | 2012-10-09 | Honeywell International Inc. | Methods systems and apparatus for analyzing complex systems via prognostic reasoning |
US20110153362A1 (en) | 2009-12-17 | 2011-06-23 | Valin David A | Method and mechanism for identifying protecting, requesting, assisting and managing information |
US20150310497A1 (en) | 2009-12-17 | 2015-10-29 | David Valin | Method and process for registration, creation and management of micro shares of real or intangible properties and advertisements in a network system |
US8670985B2 (en) | 2010-01-13 | 2014-03-11 | Apple Inc. | Devices and methods for identifying a prompt corresponding to a voice input in a sequence of prompts |
US8903716B2 (en) | 2010-01-18 | 2014-12-02 | Apple Inc. | Personalized vocabulary for digital assistant |
US8731942B2 (en) | 2010-01-18 | 2014-05-20 | Apple Inc. | Maintaining context information between user interactions with a voice assistant |
US8706503B2 (en) | 2010-01-18 | 2014-04-22 | Apple Inc. | Intent deduction based on previous user interactions with voice assistant |
US8799000B2 (en) | 2010-01-18 | 2014-08-05 | Apple Inc. | Disambiguation based on active input elicitation by intelligent automated assistant |
US8670979B2 (en) | 2010-01-18 | 2014-03-11 | Apple Inc. | Active input elicitation by intelligent automated assistant |
US8892446B2 (en) | 2010-01-18 | 2014-11-18 | Apple Inc. | Service orchestration for intelligent automated assistant |
US8660849B2 (en) | 2010-01-18 | 2014-02-25 | Apple Inc. | Prioritizing selection criteria by automated assistant |
US9213821B2 (en) | 2010-02-24 | 2015-12-15 | Infosys Limited | System and method for monitoring human interaction |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US9190062B2 (en) | 2010-02-25 | 2015-11-17 | Apple Inc. | User profiling for voice input processing |
US20110218855A1 (en) | 2010-03-03 | 2011-09-08 | Platformation, Inc. | Offering Promotions Based on Query Analysis |
US20130054506A1 (en) | 2010-04-23 | 2013-02-28 | Siemens Aktiengesellschaft | Method and device for controlling an industrial system |
US20150142465A1 (en) | 2010-04-29 | 2015-05-21 | The Regents Of The University Of California | Pathway recognition algorithm using data integration on genomic models (paradigm) |
US20120158391A1 (en) | 2010-04-29 | 2012-06-21 | The Regents of the University of California, a U.S. entity | Pathway recognition algorithm using data integration on genomic models (PAGADIGM) |
US9160738B2 (en) | 2010-05-27 | 2015-10-13 | Microsoft Corporation | Delegation-based authorization |
US20110314075A1 (en) | 2010-06-18 | 2011-12-22 | Nokia Corporation | Method and apparatus for managing distributed computations within a computation space |
US8713021B2 (en) | 2010-07-07 | 2014-04-29 | Apple Inc. | Unsupervised document clustering using latent semantic density analysis |
US8719006B2 (en) | 2010-08-27 | 2014-05-06 | Apple Inc. | Combined statistical and rule-based part-of-speech tagging for text-to-speech synthesis |
US9047565B2 (en) | 2010-09-17 | 2015-06-02 | Fluor Technologies Corporation | Intelligent plant development library environment |
US20120072386A1 (en) | 2010-09-17 | 2012-03-22 | Fluor Technologies Corporation | Intelligent Plant Development Library Environment |
US9075783B2 (en) | 2010-09-27 | 2015-07-07 | Apple Inc. | Electronic device with text error correction based on voice recognition data |
US8719014B2 (en) | 2010-09-27 | 2014-05-06 | Apple Inc. | Electronic device with text error correction based on voice recognition data |
US20120101793A1 (en) | 2010-10-22 | 2012-04-26 | Airbus Operations (S.A.S.) | Method, devices and computer program for assisting in the diagnostic of an aircraft system, using failure condition graphs |
US8996340B2 (en) | 2010-10-22 | 2015-03-31 | Airbus S.A.S. | Method, devices and computer program for assisting in the diagnostic of an aircraft system, using failure condition graphs |
US20120143013A1 (en) | 2010-11-03 | 2012-06-07 | Davis Iii Robert Thompson | Proactive Patient Health Care Inference Engines and Systems |
US20120185330A1 (en) | 2011-01-14 | 2012-07-19 | Platformation, Inc. | Discovery and Publishing Among Multiple Sellers and Multiple Buyers |
US8825642B2 (en) | 2011-01-27 | 2014-09-02 | Electronic Entertainment Design And Research | Game recommendation engine for mapping games to disabilities |
US20120197751A1 (en) | 2011-01-27 | 2012-08-02 | Electronic Entertainment Design And Research | Product recommendations and weighting optimization systems |
US20120197874A1 (en) | 2011-01-27 | 2012-08-02 | Electronic Entertainment Design And Research | Game recommendation engine for mapping games to disabilities |
US20120197653A1 (en) | 2011-01-27 | 2012-08-02 | Electronic Entertainment Design And Research | Brand identification, systems and methods |
US8781836B2 (en) | 2011-02-22 | 2014-07-15 | Apple Inc. | Hearing assistance system for providing consistent human speech |
US20120226650A1 (en) | 2011-03-03 | 2012-09-06 | Nicholas Witchey | Black Box Innovation, Systems and Methods |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US20140129504A1 (en) | 2011-03-22 | 2014-05-08 | Patrick Soon-Shiong | Reasoning Engines |
US9262719B2 (en) | 2011-03-22 | 2016-02-16 | Patrick Soon-Shiong | Reasoning engines |
US9240955B1 (en) | 2011-05-02 | 2016-01-19 | Board Of Supervisors Of Louisiana State University And Agriculture And Mechanical College | System and Architecture for Robust Management of Resources in a Wide-Area Network |
US8572290B1 (en) | 2011-05-02 | 2013-10-29 | Board Of Supervisors Of Louisiana State University And Agricultural And Mechanical College | System and architecture for robust management of resources in a wide-area network |
US20120301864A1 (en) | 2011-05-26 | 2012-11-29 | International Business Machines Corporation | User interface for an evidence-based, hypothesis-generating decision support system |
US9153142B2 (en) | 2011-05-26 | 2015-10-06 | International Business Machines Corporation | User interface for an evidence-based, hypothesis-generating decision support system |
US8812294B2 (en) | 2011-06-21 | 2014-08-19 | Apple Inc. | Translating phrases from one language into another using an order-based set of declarative rules |
US20140149337A1 (en) | 2011-07-12 | 2014-05-29 | Siemens Aktiengesellschaft | Actuation of a technical system |
US20130203038A1 (en) | 2011-08-10 | 2013-08-08 | Learningmate Solutions Private Limited | System, method and apparatus for managing education and training workflows |
US8706472B2 (en) | 2011-08-11 | 2014-04-22 | Apple Inc. | Method for disambiguating multiple readings in language conversion |
US8762156B2 (en) | 2011-09-28 | 2014-06-24 | Apple Inc. | Speech recognition repair using contextual information |
US20140277755A1 (en) | 2011-10-28 | 2014-09-18 | Siemens Aktiengesellschaft | Control of a machine |
US20130110573A1 (en) | 2011-11-02 | 2013-05-02 | Fluor Technologies Corporation | Identification and optimization of over-engineered components |
US20140249875A1 (en) | 2011-12-21 | 2014-09-04 | International Business Machines Corporation | Detecting cases with conflicting rules |
US20140358865A1 (en) | 2011-12-28 | 2014-12-04 | Hans-Gerd Brummel | Processing a technical system |
US20130204813A1 (en) | 2012-01-20 | 2013-08-08 | Fluential, Llc | Self-learning, context aware virtual assistants, systems and methods |
US20140214460A1 (en) | 2012-02-15 | 2014-07-31 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US20130212130A1 (en) | 2012-02-15 | 2013-08-15 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US20140129693A1 (en) | 2012-02-15 | 2014-05-08 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US20140129557A1 (en) | 2012-02-15 | 2014-05-08 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US20130212065A1 (en) | 2012-02-15 | 2013-08-15 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US20130211841A1 (en) | 2012-02-15 | 2013-08-15 | Fluential, Llc | Multi-Dimensional Interactions and Recall |
US20140195664A1 (en) | 2012-02-15 | 2014-07-10 | Flybits, Inc. | Zone Oriented Applications, Systems and Methods |
US20150039648A1 (en) | 2012-04-12 | 2015-02-05 | Tata Consultancy Services Limited | System and a method for reasoning and running continuous queries over data streams |
US8768869B1 (en) | 2012-04-19 | 2014-07-01 | The United States Of America As Represented By The Secretary Of The Navy | BRIAN: a basic regimen for intelligent analysis using networks |
US9258306B2 (en) | 2012-05-11 | 2016-02-09 | Infosys Limited | Methods for confirming user interaction in response to a request for a computer provided service and devices thereof |
US8775442B2 (en) | 2012-05-15 | 2014-07-08 | Apple Inc. | Semantic search using a single-source semantic model |
US8965818B2 (en) | 2012-05-16 | 2015-02-24 | Siemens Aktiengesellschaft | Method and system for supporting a clinical diagnosis |
US20130310653A1 (en) | 2012-05-16 | 2013-11-21 | Sonja Zillner | Method and system for supporting a clinical diagnosis |
US20150261825A1 (en) | 2012-07-03 | 2015-09-17 | Siemens Aktiengesellschaft | Determination of the Suitability of a Resource |
US8935167B2 (en) | 2012-09-25 | 2015-01-13 | Apple Inc. | Exemplar-based latent perceptual modeling for automatic speech recognition |
US20140223561A1 (en) | 2013-02-05 | 2014-08-07 | Hackproof Technologies Inc. | Domain-specific Hardwired Symbolic Machine |
US20150096026A1 (en) | 2013-03-15 | 2015-04-02 | Cyberricade, Inc. | Cyber security |
US20140270467A1 (en) | 2013-03-18 | 2014-09-18 | Kenneth Gerald Blemel | System for Anti-Tamper Parcel Packaging, Shipment, Receipt, and Storage |
US20150254330A1 (en) | 2013-04-11 | 2015-09-10 | Oracle International Corporation | Knowledge-intensive data processing system |
US20150154178A1 (en) | 2013-04-19 | 2015-06-04 | Empire Technology Development Llc | Coarse semantic data set enhancement for a reasoning task |
US20150199607A1 (en) | 2013-05-31 | 2015-07-16 | Empire Technology Development Llc | Incremental reasoning based on scalable and dynamical semantic data |
US20150046388A1 (en) | 2013-08-07 | 2015-02-12 | Wright State University | Semantic perception |
US20150079556A1 (en) | 2013-09-13 | 2015-03-19 | Maarit LAITINEN | Teaching means for mathematics |
US20150269639A1 (en) | 2014-03-03 | 2015-09-24 | Avi Mistriel | Referent-Centric Social Networking |
Non-Patent Citations (76)
Title |
---|
Aliseda-LLera "Seeking Explanations Abduction in Logic Philosophy of Science and Artificial Intelligence", Dissertation, 1997, (Chapter 2,) pp. 44. * |
Apt, Krzysztof R., and Marc Bezem. "Acyclic programs." New generation computing 9.3-4 (1991): 335-363. |
Baader, Franz and Brandt, Sebastian and Lutz, Carsten: Pushing the EL Envelope. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005). (Professional Book Center, 2005). pp. 364-369. Isbn: 0938075934. (http://www.ijcai.org/papers/0372.pdf). |
Baader, Franz, et al. "Matching in description logics." Journal of Logic and Computation 9.3 (1999): 411-447. |
Bodlaender, et al. "On the MPA Problem in Probabilistic Networks." BNAIC'01 Sponsors 1 (2001): 71-78. |
Bylander, Tom, et al. "The computational complexity of abduction." Artificial intelligence 49.1 (1991): 25-60. |
Cal Andrea, et al. "A description logic based approach for matching user profiles." 2004 International Workshop on Description Logics. 2004. |
Charniak, Eugene. Introduction to artificial intelligence. Pearson Education India, 1985. |
Chinese Office Action (with English language translation) issued in counterpart Chinese Application No. 201280044282.4 dated Jun. 30, 2015. |
Christiansen, Henning, and Veronica Dahl. "HYPROLOG: A new logic programming language with assumptions and abduction." Logic Programming. Springer Berlin Heidelberg, 2005. 159-173. |
Colucci, Simona, et al. "Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace." Electronic Commerce Research and Applications 4.4 (2006): 345-361. |
Console, Luca, Daniele Theseider Dupré, and Pietro Torasso. "A Theory of Diagnosis for Incomplete Causal Models." IJCAI. 1989. |
Denecker, Marc, and Antonis Kakas. "Abduction in logic programming." Computational logic: Logic programming and beyond. Springer Berlin Heidelberg, 2002. 402-436. |
Denecker, Marc, and Danny De Schreye. "Representing incomplete knowledge in abductive logic programming." Journal of Logic and Computation 5.5 (1995): 553-577. |
Denecker, Marc, and Danny De Schreye. "SLDNFA: an abductive procedure for abductive logic programs." The journal of logic programming 34.2 (1998): 111-167. |
Denecker, Marc, Lode Missiaen, and Maurice Bruynooghe. "Temporal Reasoning with Abductive Event Calculus." ECAI. 1992. |
Di Noia, Tommaso, et al. "Abductive matchmaking using description logics." IJCAI. vol. 3. 2003. |
Dung, Phan Mink "Negations as Hypotheses: An Abductive Foundation for Logic Programming." ICLP. 1991. |
Dung, Phan Mink "Representing Actions in Logic Programming and Its Applications in Database Updates." ICLP. vol. 93. 1993. |
Eco, Umberto. "The Sign of Three: Dupin, Holmes, Peirce Advances in." (1983). |
Eiter, Thomas, and Georg Gottlob. "The complexity of logic-based abduction." Journal of the ACM (JACM) 42.1 (1995): 3-42. |
Eiter, Thomas, and Kazuhisa Makino. "On computing all abductive explanations." AAAI/IAAI. 2002. |
Eiter, Thomas, Georg Gottlob, and Nicola Leone. "Abduction from logic programs: Semantics and complexity." Theoretical computer science 189.1 (1997): 129-177. |
Eiter, Thomas, Georg Gottlob, and Nicola Leone. "Semantics and complexity of abduction from default theories." Artificial Intelligence 90.1 (1997): 177-223. |
Endriss, Ulrich, et al. "The CIFF proof procedure for abductive logic programming with constraints." Logics in Artificial Intelligence. Springer Berlin Heidelberg, 2004. 31-43. |
Eshghi, Kave. "Abductive Planning with Event Calculus." ICLP/SLP. 1988. |
Eshghi, Kaye. "A Tractable Class of Abduction Problems." IJCAI. 1993. |
Gentner, Dedre. "Analogical inference and analogical access." Analogica (1988): 63-88. |
Greiner, Russell, Barbara A. Smith, and Ralph W. Wilkerson. "A correction to the algorithm in Reiter's theory of diagnosis." Artificial Intelligence 41.1 (1989): 79-88. |
Harman, Gilbert H. "The inference to the best explanation." The Philosophical Review 74.1 (1965): 88-95. |
Hirata, Kouichi. "A classification of abduction: abduction for logic programming." Machine intelligence 14. 1993. |
Hobbs, Jerry R. "Abduction in natural language understanding." Handbook of pragmatics (2004): 724-741. |
Hobbs, Jerry R., et al. "Interpretation as abduction." Proceedings of the 26th annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 1988. |
Hubauer, Thomas M., Steffen Lamparter, and Michael Pirker. "Relaxed abduction: Robust information interpretation for Incomplete models." 24th International Workshop on Description Logics. 2011. |
Inoue, Katsumi, and Chiaki Sakama. "Abductive Framework for Nonmonotonic Theory Change." IJCAI. vol. 95. 1995. |
Inoue, Katsumi. "Hypothetical reasoning in logic programs." The Journal of Logic Programming 18.3 (1994): 191-227. |
International Search Report dated Oct. 10, 2012 issued in corresponding International patent application No. PCT/EP2012/062815. |
Jin et al. "Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies", IEEE SMC, vol. 38, No. 3, 2008, pp. 397-415. * |
Josephson, John R., and Susan G. Josephson. Abductive inference: Computation, philosophy, technology. Cambridge University Press, 1996. |
Kakas, Antonis C., Antonia Michael, and Costas Mourlas. "ACLP: Abductive constraint logic programming." The Journal of Logic Programming 44.1 (2000): 129-177. |
Kakas, Antonis C., Robert A. Kowalski, and Francesca Toni. "Abductive logic programming." Journal of logic and computation 2.6 (1992): 719-770. |
Kakas, Antonis C., Robert A. Kowalski, and Francesca Toni. "The role of abduction in logic programming." Handbook of logic in artificial intelligence and logic programming 5 (1998): 235-324. |
Kowalski, Robert A., Francesca Toni, and Gerhard Wetzel. "Executing suspended logic programs." Fundamenta Informaticae 34.3 (1998): 203-224. |
Lecue F. et al.; "Applying Abduction in Semantic Web Service Composition"; WEB SERVICES, 2007. ICWS 2007, IEEE, PI; pp. 94-101; ISBN: 978-0-7695-2924-0; XP031119904; 2007; Jul. 1, 2007. |
Lin, Fangzhen, and Jia-Huai You. "Abduction in logic programming: A new definition and an abductive procedure based on rewriting." Artificial Intelligence 140.1 (2002): 175-205. |
Magnani, Lorenzo. "Abductive reasoning: philosophical and educational perspectives in medicine." Advanced models of cognition for medical training and practice. Springer Berlin Heidelberg, 1992. 21-41. |
Mayer, Marta Cialdea, and Fiora Pirri "First order abduction via tableau and sequent calculi." Logic Journal of IGPL 1.1 (1993): 99-117. |
McIllraith, Sheila. "Generating tests using abduction." Proc. of KR 94 (1994): 449-460. |
McIlraith, Sheila A. "Logic-based abductive inference." Knowledge Systems Laboratory, Technical Report KSL-98-19 (1998). |
McIlraith, Sheila, and Ray Reiter. "On experiments for hypothetical reasoning." Proc. 2nd International Workshop on Principles of Diagnosis. 1992. |
McIlraith, Sheila, and Raymond Reiter. "On tests for hypothetical reasoning." Readings in model-based diagnosis (1992): 89-96. |
Ng, Hwee Tou, and Raymond J. Mooney. "On the Role of Coherence in Abductive Explanation3." (1990). |
Noia, Tommaso Di, et al. "A system for principled matchmaking in an electronic marketplace." International Journal of Electronic Commerce 8.4 (2004): 9-37. |
Paul, Gabriele. "Approaches to abductive reasoning: an overview." Artificial intelligence review 7.2 (1993): 109-152. |
Pereira, Luís Moniz, Joaquim Nunes Aparício, and José Júlio Alferes. "Hypothetical Reasoning with Well Founded Semantics." SCAI. 1991. |
Pino-Perez et al. "Preferences and explanation", Artificial Intelligence 149, 2003, pp. 1-30. * |
Pirri, Fiora, and Clara Pizzuti. "Explaining incompatibilities in data dictionary design through abduction." Data & knowledge engineering 13.2 (1994): 101-139. |
Poole, David. "A logical framework for default reasoning." Artificial intelligence 36.1 (1988): 27-47. |
Poole, David. "A methodology for using a default and abductive reasoning system." International Journal of Intelligent Systems 5.5 (1990): 521-548. |
Poole, David. "Explanation and prediction: an architecture for default and abductive reasoning." Computational Intelligence 5.2 (1989): 97-110. |
Poole, David. "Logic Programming, Abduction and Probability." FGCS. 1992. |
Poole, David. "Logic programming, abduction and probability." New Generation Computing 11.3-4 (1993): 377-400. |
Poole, David. "Normality and Faults in Logic-Based Diagnosis." IJCAI. vol. 89. 1989. |
Poole, David. "Probabilistic Horn abduction and Bayesian networks." Artificial intelligence 64.1 (1993): 81-129. |
Poole, David. "Representing diagnosis knowledge." Annals of Mathematics and Artificial Intelligence 11.1-4 (1994): 33-50. |
Poole, David. "Representing Diagnostic Knowledge for Probabilistic Horn Abduction." IJCAI. 1991. |
Pople, Harry E. "On the Mechanization of Abductive Logic." IJCAI. vol. 73. 1973. |
Ramoni, Marco, et al. "An epistemological framework for medical knowledge-based systems." Systems, Man and Cybemetics, IEEE Transactions on 22.6 (1992): 1361-1375. |
Sakama, Chiaki, and Katsumi Inoue. "Abductive logic programming and disjunctive logic programming: their relationship and transferability." The Journal of Logic Programming 44.1 (2000): 75-100. |
Sakama, Chiaki, and Katsumi Inoue. "On the Equivalence between Disjunctive and Abductive Logic Programs." ICLP. 1994. |
Skriver, A.J.V. : A classifcation of bicriterion shortest path (bsp) algorithms. Asia-Pacifc Journal of Operational Research 17, pp. 199-212 (2000). |
Stickel, Mark E. "A Prolog-like inference system for computing minimum-cost abductive explanations in natural-language interpretation." Annals of Mathematics and Artificial Intelligence 4.1-2 (1991): 89-105. |
Van Nuffelen, Bert. "A-System: Problem solving through abduction." BNAIC'01 Sponsors 1 (2001): 591-596. |
Veit, Daniel, et al. "Matchmaking for autonomous agents in electronic marketplaces." Proceedings of the fifth International conference on Autonomous agents. ACM, 2001. |
Wang, Huaiqing, Stephen Liao, and Lejian Liao. "Modeling constraint-based negotiating agents" Decision Support Systems 33.2 (2002): 201-217. |
Written Opinion dated Oct. 10, 2012 issued in corresponding International patent application No. PCT/EP2012/062815. |
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